Ch 2 Code (All)
Victoria Field
6/9/2020
Data Cleaning
getwd()## [1] "/Users/victoriafield/Library/Mobile Documents/com~apple~CloudDocs/Masters Thesis/Chapter Two CSLAP Manuscript/R - data and outputs"CSLAP<-read.csv("./raw_data/CSLAP 2012-2017 EDI Original.csv", na.strings = c("", " ", "NA", "NaN"))str(CSLAP)## 'data.frame': 5025 obs. of 47 variables:
## $ LakeID : chr "1005GLE0441" "1005GLE0441" "1005GLE0441" "1005GLE0441" ...
## $ Lake.Name : chr "Glen Lake" "Glen Lake" "Glen Lake" "Glen Lake" ...
## $ Proj..Code : num 8 8 8 8 8 8 8 8 8 8 ...
## $ Sample.Name : chr "16-08-02" "16-08-03" "16-08-04" "16-08-05" ...
## $ Sample.Date : chr "07/04/2016" "07/11/2016" "07/26/2016" "08/01/2016" ...
## $ Data.Provider : chr "CSL" "CSL" "CSL" "CSL" ...
## $ Info.Type : chr "OW" "OW" "OW" "OW" ...
## $ LocationID : chr "1005GLE0441_C" "1005GLE0441_C" "1005GLE0441_C" "1005GLE0441_C" ...
## $ Latitude : num 43.4 43.4 43.4 43.4 43.4 ...
## $ Longitude : num -73.7 -73.7 -73.7 -73.7 -73.7 ...
## $ Sample.Depth..m. : num 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 ...
## $ Secchi.Depth..m. : num 3.95 4 3.95 4 5.95 4.05 4.9 3.55 2.6 3.3 ...
## $ DO..mg.L. : logi NA NA NA NA NA NA ...
## $ Bottom.DO..mg.L. : logi NA NA NA NA NA NA ...
## $ pH : num 7.59 7.55 8.37 7.37 7.37 7.51 7.99 7.77 7.76 7.45 ...
## $ TP..mg.L. : num 0.0068 0.0081 0.0077 0.0061 0.0063 0.0067 0.0076 0.0086 NA NA ...
## $ TDP..mg.L. : logi NA NA NA NA NA NA ...
## $ Conductance..umho.cm. : num 424 363 451 406 400 ...
## $ Temperature..air..deg.C. : num 21 23 25 22 17 25 26 24 22 21 ...
## $ Temperature..water..deg.C. : num 24 26 26 26 19 27 26 23 25 22 ...
## $ TN..mg.L. : num 0.321 0.197 0.202 0.214 0.215 0.515 0.29 0.229 0.291 0.189 ...
## $ TDN..mg.L. : num NA NA NA NA NA NA NA NA NA NA ...
## $ Ammonia..mg.L. : num NA 0.028 NA 0.076 0.102 NA 0.0075 NA 0.022 0 ...
## $ NOx..mg.L. : num NA 0.0035 NA 0.034 0.029 NA 0.0035 NA 0.00275 NA ...
## $ Nitrate..mg.L. : logi NA NA NA NA NA NA ...
## $ Nitrite..mg.L. : num NA NA NA NA NA NA NA NA NA NA ...
## $ True.Color..ptu. : num 15 23 15 18 11 16 22 14 6 3 ...
## $ Chloride..mg.L. : num NA 51.7 NA NA NA NA 74.1 NA NA NA ...
## $ Calcium..mg.L. : num NA NA NA NA NA NA NA NA 21.3 NA ...
## $ Chl.a..probe...ug.L. : logi NA NA NA NA NA NA ...
## $ Extracted.Chl.a..ug.L. : num 0.8 1.7 1.5 0.6 1.1 1.7 1.5 3.8 13.3 3.2 ...
## $ UFI.SBK.FP.Chl.a..ug.L. : logi NA NA NA NA NA NA ...
## $ UFI.SBK.FP.BGA..ug.L. : logi NA NA NA NA NA NA ...
## $ X.UFI.SBK.Microcystin..ug.L..: logi NA NA NA NA NA NA ...
## $ ESF.FP.Chl.a..ug.L. : num NA NA NA NA NA NA NA NA 2.45 2.21 ...
## $ ESF.FP.BGA..ug.L. : num 0 0 0.36 0.2 0 0.17 0.19 0.42 0 0 ...
## $ ESF.Microcystin..ug.L. : chr "ND" "ND" "ND" "ND" ...
## $ HAB.Status : chr NA NA NA "No Bloom" ...
## $ Alkalinity.as.CaCO3..mg.L. : logi NA NA NA NA NA NA ...
## $ DOC..mg.L. : logi NA NA NA NA NA NA ...
## $ UV.254..1.cm. : logi NA NA NA NA NA NA ...
## $ Iron..ug.L. : logi NA NA NA NA NA NA ...
## $ Manganese..ug.L. : logi NA NA NA NA NA NA ...
## $ Arsenic..ug.L. : num NA NA NA NA NA NA NA NA NA NA ...
## $ QA : int 3 2 2 2 NA 2 2 2 3 2 ...
## $ QB : int 3 3 3 3 NA 4 3 3 3 3 ...
## $ QC : int 3 2 3 3 NA 2 2 3 3 2 ...Based on output from str() and prior knowledge of the data, to clean this dataset I need to:
-Change column names and remove units
-Identify duplicate levels of Lake_Name, we know there is a duplicate because we have 73 levels of the unique factor LakeID and 72 levels of Lake_Name
-Split Sample_Date variable into two more columns: Sample_Year and Sample_Month
-For each lake, identify how many years the observations span (If less than 3, remove from dataset)
-Clean levels of Data_Provider to one level (“CSL”)
-Clean levels of Info_Type
-Remove character strings from ESF_Microcystin_ug.L
-Read in a merge values for Mean Depth, Catchment Area to Surface Area Ratio, Dreissenid Status By Year, and %Agricultural Cover
Change column names
colnames(CSLAP)<-c("LakeID", "Lake_Name", "Proj_Code", "Sample_Name", "Sample_Date", "Data_Provider", "Info_Type", "LocationID", "Latitude", "Longitude", "Sample_Depth", "Secchi_Depth", "DO", "Bottom_DO","pH", "TP", "TDP", "Conductance", "Temp_Air", "Temp_Water", "TN", "TDN" , "Ammonia", "NOx", "Nitrate", "Nitrite", "True_Color", "Chloride", "Calcium", "Chl.a_FP", "Extracted_Chl.a", "UFI_SBK_FP_Chl.a", "UFI_SBK_FP_BGA", "UFI_SBK_Microcystin", "ESF_FP_Chl.a","ESF_FP_BGA", "ESF_Microcystin", "HAB_Status", "Alkalinity_CaCO3", "DOC", "UV_254", "Iron", "Manganese", "Arsenic", "QA", "QB", "QC")Change duplicate Lake_Name:“Forest Lake” and remove white space from Lake_Name: “Hadlock Pond”
We want the lake with LakeID:“1301FOR0441” to be called “Lake Forest” instead of “Forest Lake”. And we need to change “Hadlock Pond” to “Hadlock Pond”
CSLAP$Lake_Name<-as.character(CSLAP$Lake_Name) #first, make this column a character vector instead of factor
CSLAP[CSLAP$LakeID == "1301FOR0441", 2] <- rep("Lake Forest", 40) #then, replace the specified cells with "Lake Forest"
CSLAP[CSLAP$LakeID == "1005HAD0432", 2] <- rep("Hadlock Pond", 78) #then, replace the specified cells with "Hadlock Pond"
CSLAP$Lake_Name<-as.factor(CSLAP$Lake_Name) #last, change the column back to factor vectorCreate new columns for Sample_Year and Sample_Month
#extract sample year and month
CSLAP$Sample_Year<-as.numeric(format(as.Date(CSLAP$Sample_Date, format="%m/%d/%Y"),"%Y"))
CSLAP$Sample_Month<-as.numeric(format(as.Date(CSLAP$Sample_Date, format="%m/%d/%Y"),"%m"))For each lake, check how many years they were enrolled in CSLAP. Remove lakes with less than 3 years
table(CSLAP$LakeID, CSLAP$Sample_Year)##
## 2012 2013 2014 2015 2016 2017
## 0201CUB0115 16 16 16 16 16 16
## 0303LOR0009 8 8 0 8 8 8
## 0403RUS0146 0 0 0 0 16 16
## 0404LOO0079 4 8 8 16 16 16
## 0601CHE0213 17 17 16 16 17 16
## 0601GUI0188 16 16 16 13 17 16
## 0602BRA0154 13 12 16 0 19 16
## 0602CRO0073 12 13 4 13 17 14
## 0602EAR0146 22 26 3 20 18 16
## 0602EAT0163 13 12 0 16 16 16
## 0602ECH0081 12 17 17 17 0 0
## 0602GEN0088 12 14 16 18 18 0
## 0602HAT0155 15 17 17 0 16 16
## 0602PET0078 16 12 17 14 17 16
## 0602PLY0107 8 8 8 8 10 8
## 0602SON0072 18 2 23 21 20 16
## 0602TUL0068 10 12 16 17 18 16
## 0602ULI0067 9 12 0 18 17 16
## 0602WAR0094 8 13 16 16 17 16
## 0703CAZ0153 18 24 25 2 23 16
## 0703DER0139A 12 12 16 16 16 16
## 0703KAS0109 7 9 8 8 0 8
## 0703PAN0094 0 0 8 7 8 10
## 0703TUS0153A 12 14 0 16 14 16
## 0704CAN0286 0 0 0 0 1 0
## 0801BRA0689 16 16 16 0 16 16
## 0801EFF0426 0 8 8 0 0 0
## 0801OTT0926 0 8 0 8 8 8
## 0902EAG0083 9 8 0 8 11 9
## 0904SIL0351 10 8 8 9 8 8
## 0906BON0024 12 12 16 14 16 17
## 0906GRA0051 0 10 15 14 8 16
## 0906MIL0055 12 12 16 16 12 16
## 0906OTH0009 0 0 0 19 17 16
## 1004AUG0213 16 16 3 17 16 16
## 1004LIN0315 0 11 16 16 15 0
## 1004PLA0254 24 17 14 22 12 12
## 1005GLE0441 11 18 14 16 16 16
## 1005HAD0432 12 16 17 17 0 16
## 1005SUN0440 15 22 18 15 0 16
## 1102BAB1109 16 18 17 17 16 16
## 1102TAC1129 0 5 5 4 6 6
## 1104EAG0438 12 13 14 12 16 16
## 1104EFN0129 12 12 15 16 16 0
## 1104FOR0340 4 6 4 6 6 7
## 1104FRI0365 16 16 16 0 8 8
## 1104HUN0131 12 12 0 16 16 16
## 1104JEN0130 12 12 15 0 13 16
## 1104PAR0432 0 28 0 0 0 0
## 1104PLE0313 0 0 16 16 16 0
## 1104SAC0314 16 16 0 0 0 16
## 1104SCH0374 28 23 12 33 32 32
## 1201CAN0717 12 12 15 14 15 16
## 1201ECA0697 13 10 17 16 16 16
## 1201GAL0563 12 13 16 16 16 16
## 1201PEC0686 8 0 8 12 16 16
## 1201PLE0745 0 12 16 12 8 8
## 1301BOW0444 0 9 9 8 14 8
## 1301BUR0386C 0 13 9 15 16 16
## 1301FOR0441 0 8 8 7 9 8
## 1301IND0167 0 5 9 7 16 17
## 1301ROA0183B 9 8 0 16 10 10
## 1301SEP0826 0 16 13 15 16 10
## 1310QUE0057 13 12 17 19 14 16
## 1310ROU0080 0 6 7 7 6 8
## 1310SPR0081 0 0 16 16 16 16
## 1401ANA0251 12 14 16 14 16 14
## 1401DEV0174 0 0 16 16 17 12
## 1401SOM0268 24 24 17 16 17 16
## 1402YAN0021 0 8 8 6 0 8
## 1404OQU0383 16 16 16 16 14 16
## 1501TUX1007 16 0 16 17 12 14
## 1701LLO0708 8 8 8 7 8 8CSLAP$LakeID<-as.character(CSLAP$LakeID) #first, change to a character vector
CSLAP<-CSLAP[CSLAP$LakeID != "0403RUS0146", ]
CSLAP<-CSLAP[CSLAP$LakeID != "0704CAN0286", ]
CSLAP<-CSLAP[CSLAP$LakeID != "0801EFF0426", ]
CSLAP<-CSLAP[CSLAP$LakeID != "1104PAR0432", ]
CSLAP<-CSLAP[CSLAP$LakeID != "0602PLY0107", ] #Plymouth reservoir will also be removed because it is eutrophic
CSLAP$LakeID<-as.factor(CSLAP$LakeID)
CSLAP$LakeID<-droplevels(CSLAP$LakeID)
levels(CSLAP$LakeID)## [1] "0201CUB0115" "0303LOR0009" "0404LOO0079" "0601CHE0213" "0601GUI0188"
## [6] "0602BRA0154" "0602CRO0073" "0602EAR0146" "0602EAT0163" "0602ECH0081"
## [11] "0602GEN0088" "0602HAT0155" "0602PET0078" "0602SON0072" "0602TUL0068"
## [16] "0602ULI0067" "0602WAR0094" "0703CAZ0153" "0703DER0139A" "0703KAS0109"
## [21] "0703PAN0094" "0703TUS0153A" "0801BRA0689" "0801OTT0926" "0902EAG0083"
## [26] "0904SIL0351" "0906BON0024" "0906GRA0051" "0906MIL0055" "0906OTH0009"
## [31] "1004AUG0213" "1004LIN0315" "1004PLA0254" "1005GLE0441" "1005HAD0432"
## [36] "1005SUN0440" "1102BAB1109" "1102TAC1129" "1104EAG0438" "1104EFN0129"
## [41] "1104FOR0340" "1104FRI0365" "1104HUN0131" "1104JEN0130" "1104PLE0313"
## [46] "1104SAC0314" "1104SCH0374" "1201CAN0717" "1201ECA0697" "1201GAL0563"
## [51] "1201PEC0686" "1201PLE0745" "1301BOW0444" "1301BUR0386C" "1301FOR0441"
## [56] "1301IND0167" "1301ROA0183B" "1301SEP0826" "1310QUE0057" "1310ROU0080"
## [61] "1310SPR0081" "1401ANA0251" "1401DEV0174" "1401SOM0268" "1402YAN0021"
## [66] "1404OQU0383" "1501TUX1007" "1701LLO0708"Change levels of Data_Provider
levels(CSLAP$Data_Provider)## NULLCSLAP$Data_Provider<-as.character(CSLAP$Data_Provider)
CSLAP[CSLAP$Data_Provider == "csl", 6] <- "CSL"
CSLAP$Data_Provider<-as.factor(CSLAP$Data_Provider)
CSLAP$Data_Provider<-droplevels(CSLAP$Data_Provider)
levels(CSLAP$Data_Provider)## [1] "CSL"Change levels of Info_Type
levels(CSLAP$Info_Type)## NULLCSLAP$Info_Type<-as.character(CSLAP$Info_Type)
CSLAP[CSLAP$Info_Type == "ow", 7] <- "OW"
CSLAP[CSLAP$Info_Type == "sb", 7] <- "SB"
CSLAP$Info_Type<-as.factor(CSLAP$Info_Type)
CSLAP$Info_Type<-droplevels(CSLAP$Info_Type)
levels(CSLAP$Info_Type)## [1] "BS" "OW" "RT" "SB"Clean ESF_Microcystin vector
# ESF_Microcystin should be a numeric column,but it's listed as factor. We need to remove character values from this column
CSLAP$ESF_Microcystin[CSLAP$ESF_Microcystin == "ND"] <- NA
CSLAP$ESF_Microcystin[CSLAP$ESF_Microcystin == "no filter"] <- NA
CSLAP$ESF_Microcystin <- as.numeric(as.character(CSLAP$ESF_Microcystin))
str(CSLAP$ESF_Microcystin)## num [1:4898] NA NA NA NA NA NA NA NA NA NA ...Add TN:TP column
CSLAP$TN_TP<-CSLAP$TN/CSLAP$TPCheck for missing values
result <- lapply(CSLAP, identifyMissing)
result## $LakeID
## No problems found.
## $Lake_Name
## No problems found.
## $Proj_Code
## No problems found.
## $Sample_Name
## No problems found.
## $Sample_Date
## No problems found.
## $Data_Provider
## No problems found.
## $Info_Type
## No problems found.
## $LocationID
## No problems found.
## $Latitude
## No problems found.
## $Longitude
## No problems found.
## $Sample_Depth
## No problems found.
## $Secchi_Depth
## No problems found.
## $DO
## No problems found.
## $Bottom_DO
## No problems found.
## $pH
## No problems found.
## $TP
## No problems found.
## $TDP
## No problems found.
## $Conductance
## No problems found.
## $Temp_Air
## No problems found.
## $Temp_Water
## No problems found.
## $TN
## No problems found.
## $TDN
## No problems found.
## $Ammonia
## No problems found.
## $NOx
## No problems found.
## $Nitrate
## No problems found.
## $Nitrite
## No problems found.
## $True_Color
## No problems found.
## $Chloride
## No problems found.
## $Calcium
## No problems found.
## $Chl.a_FP
## No problems found.
## $Extracted_Chl.a
## No problems found.
## $UFI_SBK_FP_Chl.a
## No problems found.
## $UFI_SBK_FP_BGA
## No problems found.
## $UFI_SBK_Microcystin
## No problems found.
## $ESF_FP_Chl.a
## No problems found.
## $ESF_FP_BGA
## No problems found.
## $ESF_Microcystin
## No problems found.
## $HAB_Status
## No problems found.
## $Alkalinity_CaCO3
## No problems found.
## $DOC
## No problems found.
## $UV_254
## No problems found.
## $Iron
## No problems found.
## $Manganese
## No problems found.
## $Arsenic
## No problems found.
## $QA
## No problems found.
## $QB
## No problems found.
## $QC
## No problems found.
## $Sample_Year
## No problems found.
## $Sample_Month
## No problems found.
## $TN_TP
## The following suspected missing value codes enter as regular values: Inf, NaN.CSLAP$TN_TP[CSLAP$TN_TP == "Inf"] <- NA
CSLAP$TN_TP[CSLAP$TN_TP == "NaN"] <- NA
result <- lapply(CSLAP, identifyMissing)
result## $LakeID
## No problems found.
## $Lake_Name
## No problems found.
## $Proj_Code
## No problems found.
## $Sample_Name
## No problems found.
## $Sample_Date
## No problems found.
## $Data_Provider
## No problems found.
## $Info_Type
## No problems found.
## $LocationID
## No problems found.
## $Latitude
## No problems found.
## $Longitude
## No problems found.
## $Sample_Depth
## No problems found.
## $Secchi_Depth
## No problems found.
## $DO
## No problems found.
## $Bottom_DO
## No problems found.
## $pH
## No problems found.
## $TP
## No problems found.
## $TDP
## No problems found.
## $Conductance
## No problems found.
## $Temp_Air
## No problems found.
## $Temp_Water
## No problems found.
## $TN
## No problems found.
## $TDN
## No problems found.
## $Ammonia
## No problems found.
## $NOx
## No problems found.
## $Nitrate
## No problems found.
## $Nitrite
## No problems found.
## $True_Color
## No problems found.
## $Chloride
## No problems found.
## $Calcium
## No problems found.
## $Chl.a_FP
## No problems found.
## $Extracted_Chl.a
## No problems found.
## $UFI_SBK_FP_Chl.a
## No problems found.
## $UFI_SBK_FP_BGA
## No problems found.
## $UFI_SBK_Microcystin
## No problems found.
## $ESF_FP_Chl.a
## No problems found.
## $ESF_FP_BGA
## No problems found.
## $ESF_Microcystin
## No problems found.
## $HAB_Status
## No problems found.
## $Alkalinity_CaCO3
## No problems found.
## $DOC
## No problems found.
## $UV_254
## No problems found.
## $Iron
## No problems found.
## $Manganese
## No problems found.
## $Arsenic
## No problems found.
## $QA
## No problems found.
## $QB
## No problems found.
## $QC
## No problems found.
## $Sample_Year
## No problems found.
## $Sample_Month
## No problems found.
## $TN_TP
## No problems found.Read in and merge mean depth, CA:SA, dreissenids by year, and %ag
Dreissenids<-read.csv("./raw_data/Dreissenid_Status_byYear.csv", header=TRUE)
Dreissenids$Lake_Name<-as.character(Dreissenids$Lake_Name)
Dreissenids[200:204,2] <- rep("Hadlock Pond",5)
Dreissenids$Lake_Name<-as.factor(Dreissenids$Lake_Name)
CASA_MD<-read.csv("./raw_data/CASA_Mean_Depth.csv", header=TRUE)
CASA_MD$Lake_Name<-as.character(CASA_MD$Lake_Name)
CASA_MD[27,2] <- "Hadlock Pond"
CASA_MD$Lake_Name<-as.factor(CASA_MD$Lake_Name)
Ag<-read.csv("./raw_data/Percent Ag Cover.csv", header=TRUE)
Ag$Lake_Name<-as.character(Ag$Lake_Name)
Ag[27,1] <- "Hadlock Pond"
Ag$Lake_Name<-as.factor(Ag$Lake_Name)
CSLAP<-merge(CSLAP, Dreissenids, by=c("LakeID", "Lake_Name", "Sample_Year"), all.x =TRUE)
CSLAP<-merge(CSLAP, CASA_MD, by=c("LakeID", "Lake_Name"), all.x=TRUE)
CSLAP<-merge(CSLAP, Ag, by="Lake_Name", all.x=TRUE)Now we have a clean, full dataset. Next, we need to create the reduced dataset using propensity score matching
Create Reduced Dataset
Read in lake data
Lake_List<-read.csv("./raw_data/LakeList.csv", header=TRUE)
Lake_List<-merge(Lake_List, Ag, by="Lake_Name", na.rm=FALSE, all.x=TRUE) #merge %ag
Lake_List<-Lake_List[Lake_List$LakeID != "0403RUS0146",] #Remove Lake RushfordPropensity Score Estimation
m_ps <- glm(Dreissenids ~ Mean_Depth_m + CA.SA + Percent_Ag,
family = binomial(), data = Lake_List)
summary(m_ps)##
## Call:
## glm(formula = Dreissenids ~ Mean_Depth_m + CA.SA + Percent_Ag,
## family = binomial(), data = Lake_List)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.2155 -0.4089 -0.3318 -0.3144 2.4219
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.113660 0.954012 -3.264 0.00110 **
## Mean_Depth_m 0.019552 0.131026 0.149 0.88138
## CA.SA 0.005799 0.010392 0.558 0.57684
## Percent_Ag 0.068900 0.026159 2.634 0.00844 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 49.008 on 66 degrees of freedom
## Residual deviance: 40.860 on 63 degrees of freedom
## (1 observation deleted due to missingness)
## AIC: 48.86
##
## Number of Fisher Scoring iterations: 5prs_df <- data.frame(pr_score = predict(m_ps, type = "response"),
Dreissenids = m_ps$model$Dreissenids)
head(prs_df)## pr_score Dreissenids
## 1 0.04688800 0
## 2 0.04809539 0
## 3 0.05508699 0
## 4 0.16993506 0
## 5 0.05479752 0
## 6 0.07388909 0labs <- paste("Invasion Status:", c("Invaded", "Uninvaded"))
prs_df %>%
mutate(Dreissenids = ifelse(Dreissenids == 1, labs[1], labs[2])) %>%
ggplot(aes(x = pr_score)) +
geom_histogram(color = "white") +
facet_wrap(~Dreissenids) +
xlab("Probability of being invaded") +
theme_bw()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
lakes_nomiss <- Lake_List %>% # MatchIt does not allow missing values
dplyr::select(CA.SA, Mean_Depth_m, Percent_Ag, LakeID, Lake_Name, Dreissenids) %>%
na.omit()
mod_match <- matchit(Dreissenids ~ CA.SA + Mean_Depth_m + Percent_Ag,
method = "nearest", data = lakes_nomiss)
df_match <- match.data(mod_match)Extract observations that come from lakes in the reduced set to create new, clean reduced dataset
redLakes<-df_match[,c(4,6)]
redCSLAP<-merge(CSLAP, redLakes, by="LakeID")
redCSLAP<-redCSLAP[,1:54] #removed redundant Dreissenids column
colnames(redCSLAP)[51] <- "Dreissenids"Data Summaries and Histograms
Global Dataset
Data Summaries
#melt data frame by Dreissenids and Info_type so we can summarize all our variables of interest in one code chunk
CSLAP2<-CSLAP[, c(8, 13, 17, 21, 22, 28, 30, 32, 36, 37, 38, 50:54)]
meltCSLAP <- melt(CSLAP2, id=c("Dreissenids", "Info_Type"), na.rm=TRUE)
#summarize each variable split by invasion status and info
VariableSummaries<-ddply(meltCSLAP, c("Dreissenids", "Info_Type", "variable"), summarize,
Mean = mean(value, na.rm=TRUE),
Median = median(value, na.rm=TRUE),
Min = min(value, na.rm=TRUE),
Max = max(value, na.rm=TRUE),
SD = sd(value, na.rm=TRUE))
VariableSummaries## Dreissenids Info_Type variable Mean Median Min
## 1 Invaded BS TP 3.945294e-02 0.019850 0.0000000
## 2 Invaded BS Temp_Water 1.511196e+01 15.000000 5.0000000
## 3 Invaded BS CA.SA 2.788857e+01 6.507353 4.4873502
## 4 Invaded BS Mean_Depth_m 6.232740e+00 6.200000 3.7000000
## 5 Invaded BS Percent_Ag 2.046619e+01 20.000000 1.0000000
## 6 Invaded OW Secchi_Depth 4.536559e+00 4.400000 1.1000000
## 7 Invaded OW TP 1.276286e-02 0.011000 0.0036000
## 8 Invaded OW Temp_Water 2.330388e+01 24.000000 10.0000000
## 9 Invaded OW TN 5.232666e-01 0.368810 0.0000000
## 10 Invaded OW True_Color 1.295292e+01 11.000000 0.5000000
## 11 Invaded OW Calcium 2.842750e+01 24.450000 14.3000000
## 12 Invaded OW Extracted_Chl.a 2.372222e+00 1.900000 0.0500000
## 13 Invaded OW ESF_FP_Chl.a 1.945223e+00 1.600000 0.0000000
## 14 Invaded OW ESF_FP_BGA 4.813312e-01 0.100000 0.0000000
## 15 Invaded OW ESF_Microcystin 6.437536e-01 0.440000 0.3285191
## 16 Invaded OW TN_TP 4.676542e+01 31.182701 0.0000000
## 17 Invaded OW CA.SA 2.823056e+01 10.780370 4.4873502
## 18 Invaded OW Mean_Depth_m 6.233231e+00 6.200000 3.7000000
## 19 Invaded OW Percent_Ag 1.934462e+01 20.000000 1.0000000
## 20 Invaded SB Secchi_Depth 2.750000e+00 2.750000 2.7500000
## 21 Invaded SB ESF_FP_Chl.a 2.138201e+03 142.635000 0.2000000
## 22 Invaded SB ESF_FP_BGA 2.002831e+03 85.475000 0.0000000
## 23 Invaded SB ESF_Microcystin 7.493377e+01 43.840108 0.8346926
## 24 Invaded SB CA.SA 1.424389e+01 4.807692 4.6535893
## 25 Invaded SB Mean_Depth_m 5.713333e+00 7.100000 3.7000000
## 26 Invaded SB Percent_Ag 3.256667e+01 21.500000 20.0000000
## 27 Uninvaded BS Secchi_Depth 5.383333e+00 5.350000 5.1000000
## 28 Uninvaded BS TP 5.579272e-02 0.015000 0.0000000
## 29 Uninvaded BS Temp_Water 1.347582e+01 13.000000 3.0000000
## 30 Uninvaded BS TN 3.655000e-01 0.365500 0.3580000
## 31 Uninvaded BS True_Color 1.300000e+01 13.000000 9.0000000
## 32 Uninvaded BS Extracted_Chl.a 1.600000e+00 1.600000 1.6000000
## 33 Uninvaded BS ESF_FP_Chl.a 5.300000e-01 0.570000 0.3900000
## 34 Uninvaded BS ESF_FP_BGA 0.000000e+00 0.000000 0.0000000
## 35 Uninvaded BS TN_TP 1.234775e+01 12.347749 8.1978022
## 36 Uninvaded BS CA.SA 1.816096e+01 7.732739 0.7403433
## 37 Uninvaded BS Mean_Depth_m 7.083978e+00 5.500000 2.6000000
## 38 Uninvaded BS Percent_Ag 8.507919e+00 1.000000 0.0000000
## 39 Uninvaded OW Secchi_Depth 4.075063e+00 3.800000 1.0000000
## 40 Uninvaded OW TP 1.207152e-02 0.009700 0.0000000
## 41 Uninvaded OW Temp_Water 2.271985e+01 23.000000 7.0000000
## 42 Uninvaded OW TN 3.831279e-01 0.325000 0.0000000
## 43 Uninvaded OW True_Color 1.578847e+01 13.000000 0.5000000
## 44 Uninvaded OW Calcium 1.104227e+01 8.155000 1.5300000
## 45 Uninvaded OW Extracted_Chl.a 3.377997e+00 2.400000 0.0500000
## 46 Uninvaded OW ESF_FP_Chl.a 1.007138e+01 1.700000 0.0000000
## 47 Uninvaded OW ESF_FP_BGA 2.181487e+00 0.090000 0.0000000
## 48 Uninvaded OW ESF_Microcystin 1.188658e+00 0.407566 0.1600000
## 49 Uninvaded OW TN_TP 4.209054e+01 32.444444 1.0840999
## 50 Uninvaded OW CA.SA 1.883358e+01 8.945022 0.7403433
## 51 Uninvaded OW Mean_Depth_m 5.966943e+00 4.700000 0.4000000
## 52 Uninvaded OW Percent_Ag 6.657864e+00 1.000000 0.0000000
## 53 Uninvaded RT CA.SA 8.140788e+01 81.407877 81.4078774
## 54 Uninvaded RT Mean_Depth_m 1.700000e+01 17.000000 17.0000000
## 55 Uninvaded RT Percent_Ag 1.000000e+00 1.000000 1.0000000
## 56 Uninvaded SB Secchi_Depth 3.600000e+00 2.500000 1.7000000
## 57 Uninvaded SB ESF_FP_Chl.a 2.791198e+03 171.710000 0.2800000
## 58 Uninvaded SB ESF_FP_BGA 2.309770e+03 26.870000 0.0000000
## 59 Uninvaded SB ESF_Microcystin 2.086218e+03 100.355000 0.8100000
## 60 Uninvaded SB CA.SA 9.269072e+00 6.057692 0.7403433
## 61 Uninvaded SB Mean_Depth_m 5.083735e+00 4.600000 0.4000000
## 62 Uninvaded SB Percent_Ag 9.583851e+00 4.000000 0.0000000
## Max SD
## 1 0.37420 5.324365e-02
## 2 25.00000 5.298994e+00
## 3 189.85507 5.436477e+01
## 4 9.90000 1.727590e+00
## 5 48.00000 1.550414e+01
## 6 9.10000 1.395980e+00
## 7 0.06640 7.093941e-03
## 8 36.00000 2.860583e+00
## 9 4.12400 4.399775e-01
## 10 55.00000 9.245752e+00
## 11 57.80000 1.133176e+01
## 12 13.30000 1.839573e+00
## 13 12.15000 1.634573e+00
## 14 10.36000 9.835983e-01
## 15 1.84000 5.345261e-01
## 16 240.59524 4.110511e+01
## 17 189.85507 5.391221e+01
## 18 9.90000 1.697457e+00
## 19 48.00000 1.529962e+01
## 20 2.75000 NA
## 21 31507.50000 6.226951e+03
## 22 31507.50000 6.221888e+03
## 23 361.24104 9.244751e+01
## 24 189.85507 4.064373e+01
## 25 9.90000 1.901513e+00
## 26 48.00000 1.359881e+01
## 27 5.70000 3.013857e-01
## 28 3.90960 1.657730e-01
## 29 29.00000 5.807561e+00
## 30 0.37300 1.060660e-02
## 31 17.00000 4.000000e+00
## 32 1.60000 NA
## 33 0.63000 1.249000e-01
## 34 0.00000 0.000000e+00
## 35 16.49770 5.868911e+00
## 36 210.42471 3.484989e+01
## 37 21.30000 4.142202e+00
## 38 44.00000 1.271529e+01
## 39 13.60000 1.697343e+00
## 40 0.21590 1.094758e-02
## 41 34.00000 3.428063e+00
## 42 12.93200 4.134485e-01
## 43 100.00000 1.073302e+01
## 44 58.30000 9.711479e+00
## 45 63.20000 3.682886e+00
## 46 3438.50000 1.476402e+02
## 47 2564.00000 5.420867e+01
## 48 52.86975 5.577302e+00
## 49 7936.00000 1.705462e+02
## 50 210.42471 3.385397e+01
## 51 21.30000 4.112813e+00
## 52 44.00000 1.106120e+01
## 53 81.40788 NA
## 54 17.00000 NA
## 55 1.00000 NA
## 56 6.60000 2.628688e+00
## 57 74781.25000 9.683792e+03
## 58 74781.25000 9.173332e+03
## 59 39757.12502 7.757750e+03
## 60 54.94505 7.784528e+00
## 61 13.00000 2.380611e+00
## 62 41.00000 1.102359e+01#I will pull out only the values that I need to make copying to a word document "easy"
VariableSummaries[c(1, 6:11,15:18, 20:22, 27, 38:34, 47:50, 55:57),]## Dreissenids Info_Type variable Mean Median Min
## 1 Invaded BS TP 3.945294e-02 0.019850 0.0000000
## 6 Invaded OW Secchi_Depth 4.536559e+00 4.400000 1.1000000
## 7 Invaded OW TP 1.276286e-02 0.011000 0.0036000
## 8 Invaded OW Temp_Water 2.330388e+01 24.000000 10.0000000
## 9 Invaded OW TN 5.232666e-01 0.368810 0.0000000
## 10 Invaded OW True_Color 1.295292e+01 11.000000 0.5000000
## 11 Invaded OW Calcium 2.842750e+01 24.450000 14.3000000
## 15 Invaded OW ESF_Microcystin 6.437536e-01 0.440000 0.3285191
## 16 Invaded OW TN_TP 4.676542e+01 31.182701 0.0000000
## 17 Invaded OW CA.SA 2.823056e+01 10.780370 4.4873502
## 18 Invaded OW Mean_Depth_m 6.233231e+00 6.200000 3.7000000
## 20 Invaded SB Secchi_Depth 2.750000e+00 2.750000 2.7500000
## 21 Invaded SB ESF_FP_Chl.a 2.138201e+03 142.635000 0.2000000
## 22 Invaded SB ESF_FP_BGA 2.002831e+03 85.475000 0.0000000
## 27 Uninvaded BS Secchi_Depth 5.383333e+00 5.350000 5.1000000
## 38 Uninvaded BS Percent_Ag 8.507919e+00 1.000000 0.0000000
## 37 Uninvaded BS Mean_Depth_m 7.083978e+00 5.500000 2.6000000
## 36 Uninvaded BS CA.SA 1.816096e+01 7.732739 0.7403433
## 35 Uninvaded BS TN_TP 1.234775e+01 12.347749 8.1978022
## 34 Uninvaded BS ESF_FP_BGA 0.000000e+00 0.000000 0.0000000
## 47 Uninvaded OW ESF_FP_BGA 2.181487e+00 0.090000 0.0000000
## 48 Uninvaded OW ESF_Microcystin 1.188658e+00 0.407566 0.1600000
## 49 Uninvaded OW TN_TP 4.209054e+01 32.444444 1.0840999
## 50 Uninvaded OW CA.SA 1.883358e+01 8.945022 0.7403433
## 55 Uninvaded RT Percent_Ag 1.000000e+00 1.000000 1.0000000
## 56 Uninvaded SB Secchi_Depth 3.600000e+00 2.500000 1.7000000
## 57 Uninvaded SB ESF_FP_Chl.a 2.791198e+03 171.710000 0.2800000
## Max SD
## 1 0.37420 5.324365e-02
## 6 9.10000 1.395980e+00
## 7 0.06640 7.093941e-03
## 8 36.00000 2.860583e+00
## 9 4.12400 4.399775e-01
## 10 55.00000 9.245752e+00
## 11 57.80000 1.133176e+01
## 15 1.84000 5.345261e-01
## 16 240.59524 4.110511e+01
## 17 189.85507 5.391221e+01
## 18 9.90000 1.697457e+00
## 20 2.75000 NA
## 21 31507.50000 6.226951e+03
## 22 31507.50000 6.221888e+03
## 27 5.70000 3.013857e-01
## 38 44.00000 1.271529e+01
## 37 21.30000 4.142202e+00
## 36 210.42471 3.484989e+01
## 35 16.49770 5.868911e+00
## 34 0.00000 0.000000e+00
## 47 2564.00000 5.420867e+01
## 48 52.86975 5.577302e+00
## 49 7936.00000 1.705462e+02
## 50 210.42471 3.385397e+01
## 55 1.00000 NA
## 56 6.60000 2.628688e+00
## 57 74781.25000 9.683792e+03#Write this dataframe to a csv so the values can be easily put into a word document
write.csv(VariableSummaries, "./output_data/VariableSummaries-June2020.csv")#melt Lake List data frame by Dreissenids so we can summarize all our variables of interest in one code chunk
meltLakes <- melt(Lake_List[,c(2:6)], id=c("LakeID", "Dreissenids"), na.rm=TRUE)
#summarize each variable split by invasion status and info
LakeSummaries<-ddply(meltLakes, c("Dreissenids", "variable"), summarize,
Mean = mean(value, na.rm=TRUE),
Median = median(value, na.rm=TRUE),
Min = min(value, na.rm=TRUE),
Max = max(value, na.rm=TRUE),
SD = sd(value, na.rm=TRUE))
LakeSummaries## Dreissenids variable Mean Median Min Max SD
## 1 0 CA.SA 16.590663 8.681141 0.7403433 210.4247 30.550112
## 2 0 Mean_Depth_m 5.750000 4.700000 0.4000000 21.3000 3.859152
## 3 0 Percent_Ag 6.357627 1.000000 0.0000000 44.0000 11.095405
## 4 1 CA.SA 32.575834 8.643862 4.4873502 189.8551 63.897069
## 5 1 Mean_Depth_m 6.325000 6.400000 3.7000000 9.9000 1.918146
## 6 1 Percent_Ag 21.000000 20.000000 1.0000000 48.0000 15.874508#Write this dataframe to a csv so the values can be easily put into a word document
write.csv(LakeSummaries, "./output_data/LakeSummaries.csv")#melt data frame by Dreissenids and Info_type so we can summarize all our variables of interest in one code chunk
CSLAP3<-CSLAP[CSLAP$Info_Type == "OW", c(1, 13, 17, 32)]
CSLAP3$TP <- CSLAP3$TP*1000
meltTrophic <- melt(CSLAP3, id="Lake_Name", na.rm=TRUE)
#summarize each variable split by invasion status and info
TrophicSummaries<-ddply(meltTrophic, c("Lake_Name","variable"), summarize,
Mean = mean(value, na.rm=TRUE),
Median = median(value, na.rm=TRUE),
Min = min(value, na.rm=TRUE),
Max = max(value, na.rm=TRUE),
SD = sd(value, na.rm=TRUE))
TrophicSummaries## Lake_Name variable Mean Median Min Max
## 1 Augur Lake Secchi_Depth 3.0500000 2.8250 1.650 4.850
## 2 Augur Lake TP 21.1951220 20.3000 12.600 37.300
## 3 Augur Lake Extracted_Chl.a 7.6146341 6.5000 1.500 34.200
## 4 Babcock Lake Secchi_Depth 4.2043750 4.2250 2.030 6.000
## 5 Babcock Lake TP 9.3687500 9.0500 6.500 17.800
## 6 Babcock Lake Extracted_Chl.a 3.1680851 2.9000 0.900 8.000
## 7 Big Bowman Pond Secchi_Depth 2.1462500 2.0875 1.350 3.800
## 8 Big Bowman Pond TP 14.2400000 12.8500 8.800 24.700
## 9 Big Bowman Pond Extracted_Chl.a 7.4794872 6.6000 1.500 19.200
## 10 Bradley Brook Reservoir Secchi_Depth 6.3893750 6.1500 4.550 8.700
## 11 Bradley Brook Reservoir TP 10.0450000 9.4000 6.500 19.900
## 12 Bradley Brook Reservoir Extracted_Chl.a 1.8250000 1.8000 0.100 4.400
## 13 Brantingham Lake Secchi_Depth 3.2543750 3.3250 1.700 4.775
## 14 Brantingham Lake TP 9.2325000 9.0500 5.300 15.000
## 15 Brantingham Lake Extracted_Chl.a 5.4425000 4.6500 0.300 14.400
## 16 Burden Third Lake Secchi_Depth 4.4702703 4.3000 1.600 7.050
## 17 Burden Third Lake TP 12.5736842 9.9500 6.700 75.500
## 18 Burden Third Lake Extracted_Chl.a 3.7162162 2.5000 0.500 17.800
## 19 Canada Lake Secchi_Depth 4.1956522 4.2000 2.950 5.800
## 20 Canada Lake TP 5.0739130 4.7000 3.300 8.000
## 21 Canada Lake Extracted_Chl.a 2.0333333 1.9000 0.800 3.900
## 22 Cazenovia Lake Secchi_Depth 4.1708974 4.1500 1.620 7.000
## 23 Cazenovia Lake TP 19.2000000 17.1000 10.600 42.100
## 24 Cazenovia Lake Extracted_Chl.a 3.0416667 2.3000 0.900 9.300
## 25 Chenango Lake Secchi_Depth 4.8713542 4.7250 3.000 6.950
## 26 Chenango Lake TP 10.6291667 10.0000 4.800 28.400
## 27 Chenango Lake Extracted_Chl.a 2.4750000 2.0500 0.500 7.800
## 28 Craine Lake Secchi_Depth 3.1841026 2.7000 1.700 6.650
## 29 Craine Lake TP 13.1300000 10.6000 7.000 51.900
## 30 Craine Lake Extracted_Chl.a 1.9287500 1.8500 0.050 4.800
## 31 Crooked Lake Secchi_Depth 3.3519737 3.3000 2.050 4.650
## 32 Crooked Lake TP 15.1275000 14.8000 7.700 21.500
## 33 Crooked Lake Extracted_Chl.a 6.1457143 4.5000 1.300 16.700
## 34 Cuba Lake Secchi_Depth 3.0702128 2.9500 1.400 5.850
## 35 Cuba Lake TP 19.2680851 17.4000 8.900 46.900
## 36 Cuba Lake Extracted_Chl.a 7.7195652 6.6000 0.500 49.100
## 37 De Ruyter Reservoir Secchi_Depth 4.9765625 4.7250 3.250 7.700
## 38 De Ruyter Reservoir TP 14.4541667 13.1000 3.600 62.900
## 39 De Ruyter Reservoir Extracted_Chl.a 2.1645833 2.0000 0.400 4.400
## 40 Eagle Lake Secchi_Depth 6.8555556 6.9000 2.550 10.550
## 41 Eagle Lake TP 7.8818182 6.3000 4.000 32.700
## 42 Eagle Lake Extracted_Chl.a 1.0857143 1.1000 0.300 2.600
## 43 Eagle Pond Secchi_Depth 1.4178788 1.4000 1.200 1.800
## 44 Eagle Pond TP 17.3575000 12.3000 6.800 82.400
## 45 Eagle Pond Extracted_Chl.a 3.4712500 1.9500 0.050 32.500
## 46 East Caroga Lake Secchi_Depth 3.6425532 3.7000 2.450 5.000
## 47 East Caroga Lake TP 17.9000000 9.0000 5.100 110.100
## 48 East Caroga Lake Extracted_Chl.a 3.5119565 2.4000 0.050 31.500
## 49 Eaton Brook Reservoir Secchi_Depth 5.5658537 5.5000 2.650 9.100
## 50 Eaton Brook Reservoir TP 11.0024390 9.2000 5.500 66.400
## 51 Eaton Brook Reservoir Extracted_Chl.a 1.4961538 1.3000 0.050 3.600
## 52 Echo Lake Secchi_Depth 2.6585938 2.6250 1.650 4.000
## 53 Echo Lake TP 13.2000000 11.6000 8.400 31.400
## 54 Echo Lake Extracted_Chl.a 4.0209677 2.6000 0.050 17.400
## 55 Efner Lake Secchi_Depth 3.9700000 3.8000 2.700 6.700
## 56 Efner Lake TP 6.8725000 6.3000 3.300 15.900
## 57 Efner Lake Extracted_Chl.a 1.9333333 1.5000 0.400 7.800
## 58 Forest Lake Secchi_Depth 2.4459091 2.4000 1.700 3.130
## 59 Forest Lake TP 11.6939394 11.6000 7.600 21.100
## 60 Forest Lake Extracted_Chl.a 4.2242424 3.4000 0.600 16.300
## 61 Friends Lake Secchi_Depth 4.4312500 4.3500 3.100 6.950
## 62 Friends Lake TP 7.7550000 7.5000 4.100 13.600
## 63 Friends Lake Extracted_Chl.a 3.2282051 2.6000 0.200 23.400
## 64 Galway Lake Secchi_Depth 3.3627660 3.3500 2.000 4.900
## 65 Galway Lake TP 18.4680851 10.7000 7.300 152.200
## 66 Galway Lake Extracted_Chl.a 3.1108696 3.2000 0.300 6.800
## 67 Geneganslet Lake Secchi_Depth 3.0876974 2.9250 2.300 5.000
## 68 Geneganslet Lake TP 10.7600000 9.4000 5.500 47.800
## 69 Geneganslet Lake Extracted_Chl.a 2.8900000 2.2500 0.300 18.800
## 70 Glen Lake Secchi_Depth 4.0795455 4.0000 2.600 6.000
## 71 Glen Lake TP 8.1090909 7.5000 4.300 16.000
## 72 Glen Lake Extracted_Chl.a 2.5976744 1.8000 0.600 13.300
## 73 Grass Lake Secchi_Depth 3.2242424 3.0500 2.050 5.400
## 74 Grass Lake TP 16.0774194 15.5000 0.000 34.000
## 75 Grass Lake Extracted_Chl.a 2.6342857 1.3000 0.050 12.900
## 76 Guilford Lake Secchi_Depth 3.1141304 3.1750 1.000 5.800
## 77 Guilford Lake TP 22.9282609 14.9500 8.400 99.200
## 78 Guilford Lake Extracted_Chl.a 6.6322222 3.6000 0.050 24.600
## 79 Hadlock Pond Secchi_Depth 4.7918750 4.7250 3.250 8.350
## 80 Hadlock Pond TP 7.7250000 7.3500 4.800 12.200
## 81 Hadlock Pond Extracted_Chl.a 2.7925000 2.3500 0.900 6.000
## 82 Hatch Lake Secchi_Depth 5.0307692 4.8000 2.400 8.050
## 83 Hatch Lake TP 12.0575000 12.3500 5.600 16.000
## 84 Hatch Lake Extracted_Chl.a 2.9150000 2.7000 0.600 10.300
## 85 Hunt Lake Secchi_Depth 3.6850000 3.7500 2.500 6.000
## 86 Hunt Lake TP 9.7947368 7.3000 4.600 42.500
## 87 Hunt Lake Extracted_Chl.a 2.4421053 2.2000 1.000 5.600
## 88 Indian Lake Secchi_Depth 6.3558824 6.6500 3.550 7.200
## 89 Indian Lake TP 9.5611111 9.3500 6.400 14.300
## 90 Indian Lake Extracted_Chl.a 0.8705882 0.9000 0.100 2.000
## 91 Jenny Lake Secchi_Depth 3.7428378 3.6500 2.300 6.550
## 92 Jenny Lake TP 6.9974359 6.6000 4.900 11.000
## 93 Jenny Lake Extracted_Chl.a 3.2000000 2.8000 0.300 13.900
## 94 Kasoag Lake Secchi_Depth 3.7910256 3.7000 2.800 4.900
## 95 Kasoag Lake TP 13.2820513 12.8000 6.000 33.100
## 96 Kasoag Lake Extracted_Chl.a 5.3868421 2.6000 0.200 63.200
## 97 Lake Anawanda Secchi_Depth 5.9383333 6.0000 3.100 13.600
## 98 Lake Anawanda TP 6.9217391 6.2500 4.000 16.100
## 99 Lake Anawanda Extracted_Chl.a 1.9913043 1.5000 0.600 6.700
## 100 Lake Bonaparte Secchi_Depth 4.3622222 4.4000 3.250 5.800
## 101 Lake Bonaparte TP 9.4553191 7.7000 5.700 52.700
## 102 Lake Bonaparte Extracted_Chl.a 2.0340426 1.9000 0.100 5.200
## 103 Lake Devenoge Secchi_Depth 3.7076923 3.5500 2.900 4.900
## 104 Lake Devenoge TP 8.3000000 8.6500 3.400 12.500
## 105 Lake Devenoge Extracted_Chl.a 2.9333333 2.5000 0.500 7.200
## 106 Lake Forest Secchi_Depth 3.3352564 3.2500 2.150 4.700
## 107 Lake Forest TP 13.0102564 8.5000 4.900 55.000
## 108 Lake Forest Extracted_Chl.a 5.1205128 4.0000 1.500 42.700
## 109 Lake of the Woods Secchi_Depth 5.4979167 5.4000 4.050 7.200
## 110 Lake of the Woods TP 7.8458333 7.2500 5.100 20.400
## 111 Lake of the Woods Extracted_Chl.a 1.0708333 1.1000 0.100 2.000
## 112 Lake Petonia Secchi_Depth 5.0825000 5.3500 2.150 7.650
## 113 Lake Petonia TP 9.3638298 8.8000 4.200 19.900
## 114 Lake Petonia Extracted_Chl.a 1.8304348 1.4500 0.200 8.400
## 115 Lake Placid Secchi_Depth 7.7070732 7.4000 5.130 10.650
## 116 Lake Placid TP 4.5440000 4.2000 3.000 11.500
## 117 Lake Placid Extracted_Chl.a 1.8659574 1.6000 0.600 6.300
## 118 Lake Pleasant Secchi_Depth 4.5456522 4.6000 3.600 5.750
## 119 Lake Pleasant TP 7.0125000 6.9500 5.200 9.300
## 120 Lake Pleasant Extracted_Chl.a 2.5083333 2.3000 0.800 8.000
## 121 Lake Sunnyside Secchi_Depth 6.1452564 6.3000 4.300 7.280
## 122 Lake Sunnyside TP 12.9025641 12.0000 8.100 23.200
## 123 Lake Sunnyside Extracted_Chl.a 2.1631579 1.4500 0.300 13.200
## 124 Lincoln Pond Secchi_Depth 4.1883333 4.1750 2.800 5.650
## 125 Lincoln Pond TP 7.4533333 6.6500 4.800 19.800
## 126 Lincoln Pond Extracted_Chl.a 1.8533333 1.7500 0.800 4.700
## 127 Little Long Pond Secchi_Depth 3.0212766 3.1000 1.250 4.350
## 128 Little Long Pond TP 31.9021277 19.3000 10.600 215.900
## 129 Little Long Pond Extracted_Chl.a 1.9891489 1.6000 0.300 9.100
## 130 Loon Lake Secchi_Depth 4.0656250 4.0000 2.250 5.300
## 131 Loon Lake TP 12.1666667 11.5000 7.000 18.100
## 132 Loon Lake Extracted_Chl.a 2.6166667 1.1500 0.050 13.500
## 133 Lorton Lake Secchi_Depth 1.9393750 2.1000 1.100 2.700
## 134 Lorton Lake TP 19.9230769 16.6000 12.500 77.200
## 135 Lorton Lake Extracted_Chl.a 5.6473684 3.8500 1.100 27.700
## 136 Millsite Lake Secchi_Depth 7.5055556 7.5500 5.150 9.650
## 137 Millsite Lake TP 8.6086957 7.8500 4.500 20.600
## 138 Millsite Lake Extracted_Chl.a 1.5622222 1.3000 0.300 4.400
## 139 Oquaga Lake Secchi_Depth 8.1382979 7.9000 4.850 11.550
## 140 Oquaga Lake TP 5.3872340 4.8000 3.200 14.600
## 141 Oquaga Lake Extracted_Chl.a 0.9467391 0.8500 0.050 2.600
## 142 Otter Lake Secchi_Depth 1.5046875 1.5000 1.300 1.750
## 143 Otter Lake TP 14.0625000 13.3000 8.000 30.100
## 144 Otter Lake Extracted_Chl.a 4.5193548 4.6000 1.100 10.100
## 145 Panther Lake Secchi_Depth 2.1087500 1.9750 1.250 5.000
## 146 Panther Lake TP 15.4400000 14.0000 7.700 58.000
## 147 Panther Lake Extracted_Chl.a 5.7465517 5.4000 0.050 12.500
## 148 Peck Lake Secchi_Depth 3.4047794 3.2500 2.750 4.500
## 149 Peck Lake TP 6.5475000 6.4000 0.000 9.900
## 150 Peck Lake Extracted_Chl.a 3.0138889 2.5000 1.300 9.100
## 151 Pleasant Lake Secchi_Depth 4.2594595 4.3000 3.100 5.200
## 152 Pleasant Lake TP 7.3864865 7.1000 4.400 18.700
## 153 Pleasant Lake Extracted_Chl.a 2.2305556 2.1000 0.800 3.900
## 154 Queechy Lake Secchi_Depth 5.6402174 5.5875 3.150 9.050
## 155 Queechy Lake TP 8.7350000 8.2500 5.300 15.000
## 156 Queechy Lake Extracted_Chl.a 1.9372340 1.4000 0.050 17.500
## 157 Roaring Brook Lake Secchi_Depth 2.7923077 2.8000 1.500 4.300
## 158 Roaring Brook Lake TP 12.9700000 12.5000 8.500 21.500
## 159 Roaring Brook Lake Extracted_Chl.a 3.6910256 3.3000 0.050 9.500
## 160 Round Pond Secchi_Depth 2.3926471 2.4000 1.600 3.850
## 161 Round Pond TP 10.9529412 10.9500 6.000 20.400
## 162 Round Pond Extracted_Chl.a 3.6787879 2.7000 0.500 10.100
## 163 Sacandaga Lake Secchi_Depth 4.4875000 4.5250 3.450 5.450
## 164 Sacandaga Lake TP 9.0125000 8.4000 6.000 17.500
## 165 Sacandaga Lake Extracted_Chl.a 3.5500000 3.3500 1.800 8.300
## 166 Schroon Lake Secchi_Depth 3.9670000 3.8000 2.450 7.550
## 167 Schroon Lake TP 8.4921053 6.5000 0.000 51.600
## 168 Schroon Lake Extracted_Chl.a 1.6392157 1.6000 0.050 6.500
## 169 Sepasco Lake Secchi_Depth 3.1009091 3.0500 1.725 4.700
## 170 Sepasco Lake TP 17.6000000 16.4500 10.400 28.800
## 171 Sepasco Lake Extracted_Chl.a 5.1500000 4.3000 0.100 15.000
## 172 Silver Lake Secchi_Depth 3.9240909 4.0000 2.300 4.900
## 173 Silver Lake TP 9.6208333 8.7000 5.900 18.300
## 174 Silver Lake Extracted_Chl.a 4.7978723 3.9000 0.900 23.200
## 175 Somerset Lake Secchi_Depth 4.8375000 4.7250 2.950 7.900
## 176 Somerset Lake TP 10.1212766 9.9000 3.300 15.500
## 177 Somerset Lake Extracted_Chl.a 2.2085106 1.7000 0.200 9.700
## 178 Song Lake Secchi_Depth 2.9956757 2.9000 1.850 4.750
## 179 Song Lake TP 16.4662500 16.0500 0.450 27.600
## 180 Song Lake Extracted_Chl.a 3.9850000 3.9500 0.100 8.200
## 181 Spring Lake Secchi_Depth 2.5145313 2.3750 1.750 4.700
## 182 Spring Lake TP 12.5375000 9.1000 5.700 43.100
## 183 Spring Lake Extracted_Chl.a 1.8046875 1.3500 0.050 13.600
## 184 Taconic Lake Secchi_Depth 4.3698077 4.3625 3.100 6.250
## 185 Taconic Lake TP 6.4583333 6.1500 0.500 14.000
## 186 Taconic Lake Extracted_Chl.a 1.3437500 0.9500 0.050 6.300
## 187 Tully Lake Secchi_Depth 4.1564103 3.9500 2.500 6.750
## 188 Tully Lake TP 14.8000000 14.6000 0.000 26.100
## 189 Tully Lake Extracted_Chl.a 4.5382979 4.2000 0.200 10.600
## 190 Tuscarora Lake Secchi_Depth 4.9038462 4.7000 1.100 8.800
## 191 Tuscarora Lake TP 11.3846154 10.4000 4.700 21.200
## 192 Tuscarora Lake Extracted_Chl.a 2.5270270 1.6000 0.200 11.200
## 193 Tuxedo Lake Secchi_Depth 3.1998649 2.9000 1.850 8.500
## 194 Tuxedo Lake TP 20.4085714 17.3000 9.300 53.400
## 195 Tuxedo Lake Extracted_Chl.a 5.8783784 5.2000 0.800 17.900
## 196 Upper Little York Lake Secchi_Depth 4.1833333 4.2500 2.000 6.900
## 197 Upper Little York Lake TP 13.2421053 12.0000 8.200 28.700
## 198 Upper Little York Lake Extracted_Chl.a 2.1445946 1.8000 0.050 7.800
## 199 Warn Lake Secchi_Depth 3.4479167 3.5000 2.000 5.500
## 200 Warn Lake TP 11.7291667 10.8000 8.200 21.800
## 201 Warn Lake Extracted_Chl.a 4.3413043 4.3500 1.100 10.400
## 202 Yankee Lake Secchi_Depth 3.2118333 3.1375 2.300 4.900
## 203 Yankee Lake TP 18.1433333 13.8000 7.900 71.900
## 204 Yankee Lake Extracted_Chl.a 2.9533333 2.9500 0.500 5.800
## SD
## 1 0.6883648
## 2 5.3848840
## 3 5.6200783
## 4 0.8409453
## 5 2.2221473
## 6 1.8086875
## 7 0.4742251
## 8 3.6338720
## 9 4.3201596
## 10 1.0150926
## 11 2.8473965
## 12 1.0637982
## 13 0.8669444
## 14 2.0662598
## 15 3.3057749
## 16 1.1980574
## 17 11.2707391
## 18 3.4994852
## 19 0.6966448
## 20 1.1780275
## 21 0.8121240
## 22 0.9247061
## 23 6.9674809
## 24 1.9895979
## 25 0.9619764
## 26 3.8966411
## 27 1.5408619
## 28 1.1590779
## 29 8.3979912
## 30 1.2660198
## 31 0.7415374
## 32 2.8976549
## 33 3.9799012
## 34 1.0072379
## 35 7.8332376
## 36 7.6223245
## 37 1.1783667
## 38 8.0962024
## 39 1.0115271
## 40 1.4249092
## 41 4.9807197
## 42 0.4760464
## 43 0.1218031
## 44 13.9405678
## 45 5.3671351
## 46 0.6035348
## 47 21.4132933
## 48 5.0588886
## 49 1.3442674
## 50 9.3562943
## 51 0.8437373
## 52 0.7245858
## 53 4.7263162
## 54 4.1490114
## 55 0.8134920
## 56 2.4990242
## 57 1.3387609
## 58 0.3904834
## 59 2.7789318
## 60 3.4314747
## 61 0.7747363
## 62 1.6903876
## 63 3.5227792
## 64 0.6941109
## 65 24.3472622
## 66 1.4914912
## 67 0.5359175
## 68 6.7842124
## 69 2.7956743
## 70 0.8651064
## 71 2.4129320
## 72 2.5160900
## 73 0.8318955
## 74 5.4548272
## 75 3.2150101
## 76 1.2988312
## 77 20.9236991
## 78 6.7364945
## 79 1.0187723
## 80 1.7512999
## 81 1.1899984
## 82 1.2954686
## 83 2.3474905
## 84 1.7700572
## 85 0.7298753
## 86 8.1229720
## 87 0.9027381
## 88 0.8882712
## 89 2.0875369
## 90 0.4700594
## 91 0.9240885
## 92 1.6329642
## 93 2.1791394
## 94 0.4805249
## 95 4.6929326
## 96 10.3917891
## 97 1.6701218
## 98 2.2867651
## 99 1.5665711
## 100 0.6353377
## 101 7.0592772
## 102 0.9571871
## 103 0.5949273
## 104 1.7522399
## 105 1.7642686
## 106 0.7114135
## 107 11.3368935
## 108 6.4346586
## 109 0.8060207
## 110 3.1076314
## 111 0.4920933
## 112 1.2116146
## 113 2.8993572
## 114 1.3817653
## 115 1.3064159
## 116 1.5009058
## 117 0.9805226
## 118 0.5291316
## 119 1.2173787
## 120 1.3454971
## 121 0.7997121
## 122 3.5050706
## 123 2.3340512
## 124 0.7198759
## 125 3.1453012
## 126 0.8041845
## 127 0.7120282
## 128 42.5465857
## 129 1.5502634
## 130 0.7792558
## 131 2.7482274
## 132 3.2264419
## 133 0.4226582
## 134 10.8740024
## 135 5.3553074
## 136 0.9994443
## 137 3.3943630
## 138 0.9291465
## 139 1.3407478
## 140 1.9935349
## 141 0.4770747
## 142 0.1042325
## 143 4.3524372
## 144 2.2103423
## 145 0.6609023
## 146 8.4030618
## 147 3.3743764
## 148 0.5073514
## 149 1.7044418
## 150 1.5410855
## 151 0.3847506
## 152 3.0193048
## 153 0.9070579
## 154 1.3272201
## 155 2.3522548
## 156 2.5445481
## 157 0.6876293
## 158 3.5667410
## 159 2.1537566
## 160 0.4978944
## 161 2.8161047
## 162 2.7855383
## 163 0.5385669
## 164 2.6613682
## 165 1.5644107
## 166 1.0259073
## 167 7.8465918
## 168 1.2380353
## 169 0.6527615
## 170 4.5682636
## 171 3.7012640
## 172 0.6156442
## 173 2.8252628
## 174 3.3867061
## 175 1.2873257
## 176 2.6767060
## 177 1.8325356
## 178 0.6829452
## 179 4.6445638
## 180 1.7963710
## 181 0.6230833
## 182 9.8011109
## 183 2.4191894
## 184 0.7279224
## 185 2.6992618
## 186 1.3052830
## 187 0.8593663
## 188 3.8001120
## 189 2.1971873
## 190 1.6079320
## 191 3.2621867
## 192 2.4271209
## 193 1.2173374
## 194 9.9304159
## 195 3.4664371
## 196 1.0516653
## 197 4.7798921
## 198 1.7950466
## 199 0.8097779
## 200 3.0558600
## 201 1.7694414
## 202 0.6553908
## 203 14.4030572
## 204 1.0163265write.csv(TrophicSummaries, "./output_data/TrophicSummaries.csv")
TrophicMean<-reshape(TrophicSummaries[, c(1:3, 7)], idvar="Lake_Name", timevar="variable", times=c("Mean", "SD"), direction = "wide")
colnames(TrophicMean) <- c("Lake_Name", "Secchi_Mean", "Secchi_SD", "TP_Mean", "TP_SD", "Chlorophyll-a_Mean", "Chlorophyll-a_SD")
TrophicMean<-TrophicMean[,c(1,4, 5, 6, 7, 2, 3)]
write.csv(TrophicMean, "./output_data/TrophicMeans.csv")Histograms
First we will split our dataset based on information type
OWCSLAP<-CSLAP[CSLAP$Info_Type == "OW",]
BSCSLAP<-CSLAP[CSLAP$Info_Type == "BS",]
SBCSLAP<-CSLAP[CSLAP$Info_Type == "SB",]
redOWCSLAP<-redCSLAP[redCSLAP$Info_Type == "OW",]
redBSCSLAP<-redCSLAP[redCSLAP$Info_Type == "BS",]
redBSCSLAP<-redCSLAP[redCSLAP$Info_Type == "SB",]Open Water TP
ggplot(OWCSLAP, aes(x=TP)) +
geom_histogram(position="identity", color="black", fill="white")+
facet_wrap(~Dreissenids)+
geom_density()+
theme_minimal()+
labs(title="Open water TP", x="TP (mg/L)", y="Frequency")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 49 rows containing non-finite values (stat_bin).## Warning: Removed 49 rows containing non-finite values (stat_density).
Open Water TN
ggplot(OWCSLAP, aes(x=TN)) +
geom_histogram(position="identity", color="black", fill="white")+
facet_wrap(~Dreissenids)+
geom_density()+
theme_minimal()+
labs(title="Open water TN", x="TN (mg/L)", y="Frequency")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 66 rows containing non-finite values (stat_bin).## Warning: Removed 66 rows containing non-finite values (stat_density).
Open Water TN:TP
ggplot(OWCSLAP, aes(x=TN_TP)) +
geom_histogram(position="identity", color="black", fill="white")+
facet_wrap(~Dreissenids)+
geom_density()+
theme_minimal()+
labs(title="Open water TN:TP", x="TN:TP", y="Frequency")+
scale_x_continuous(limits=c(0,100))## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 180 rows containing non-finite values (stat_bin).## Warning: Removed 180 rows containing non-finite values (stat_density).## Warning: Removed 4 rows containing missing values (geom_bar).
Open Water Chlorophyll-a
ggplot(OWCSLAP, aes(x=Extracted_Chl.a)) +
geom_histogram(position="identity", color="black", fill="white")+
facet_wrap(~Dreissenids)+
geom_density()+
theme_minimal()+
labs(title="Open water chlorophyll a", x="Chlorophyll a (ug/L)", y="Frequency")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 114 rows containing non-finite values (stat_bin).## Warning: Removed 114 rows containing non-finite values (stat_density).
Secchi Depth
ggplot(OWCSLAP, aes(x=Secchi_Depth)) +
geom_histogram(position="identity", color="black", fill="white")+
facet_wrap(~Dreissenids)+
geom_density()+
theme_minimal()+
labs(title="Secchi Depth", x="Secchi depth (m)", y="Frequency")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 114 rows containing non-finite values (stat_bin).## Warning: Removed 114 rows containing non-finite values (stat_density).
True Color
ggplot(OWCSLAP, aes(x=True_Color)) +
geom_histogram(position="identity", color="black", fill="white")+
facet_wrap(~Dreissenids)+
geom_density()+
theme_minimal()+
labs(title="True Color", x="True color (PTU)", y="Frequency")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 95 rows containing non-finite values (stat_bin).## Warning: Removed 95 rows containing non-finite values (stat_density).
Shoreline Bloom Chlorophyll
ggplot(SBCSLAP, aes(x=ESF_FP_Chl.a)) +
geom_histogram(position="identity", color="black", fill="white")+
facet_wrap(~Dreissenids)+
geom_density()+
theme_minimal()+
labs(title="Shoreline Bloom Chlorophyll a", x="Chlorophyll a (ug/L)", y="Frequency")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 2 rows containing non-finite values (stat_bin).## Warning: Removed 2 rows containing non-finite values (stat_density).
Shoreline Bloom Microcystin
ggplot(SBCSLAP, aes(x=ESF_Microcystin)) +
geom_histogram(position="identity", color="black", fill="white")+
facet_wrap(~Dreissenids)+
geom_density()+
theme_minimal()+
labs(title="Shoreline Bloom Microcystin", x="Microcystin (ug/L)", y="Frequency")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 180 rows containing non-finite values (stat_bin).## Warning: Removed 180 rows containing non-finite values (stat_density).
Open Water Microcystin
ggplot(OWCSLAP, aes(x=ESF_Microcystin)) +
geom_histogram(position="identity", color="black", fill="white")+
facet_wrap(~Dreissenids)+
geom_density()+
theme_minimal()+
labs(title="Open Water Microcystin", x="Microcystin (ug/L)", y="Frequency")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 2634 rows containing non-finite values (stat_bin).## Warning: Removed 2634 rows containing non-finite values (stat_density).
Mean Depth
#Quickly change factor level labels for Dreissenids
Lake_List[Lake_List$Dreissenids == 0, 5] <- rep("Uninvaded", 60)
Lake_List[Lake_List$Dreissenids == 1, 5] <- rep("Invaded", 8)ggplot(Lake_List, aes(x=Mean_Depth_m)) +
geom_histogram(position="identity", color="black", fill="white")+
facet_wrap(~Dreissenids)+
geom_density()+
theme_minimal()+
labs(title="Mean Depth", x="Mean depth (m)", y="Frequency")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
CA:SA
ggplot(Lake_List, aes(x=CA.SA)) +
geom_histogram(position="identity", color="black", fill="white")+
facet_wrap(~Dreissenids)+
geom_density()+
theme_minimal()+
labs(title="Catchment Area to Surface Area Ratio", x="CA:SA", y="Frequency")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Percent Agriculture
ggplot(Lake_List, aes(x=Percent_Ag)) +
geom_histogram(position="identity", color="black", fill="white")+
facet_wrap(~Dreissenids)+
geom_density()+
theme_minimal()+
labs(title="Percent Agricultural Cover in Watershed", x="Agriculture (%)", y="Frequency")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 1 rows containing non-finite values (stat_bin).## Warning: Removed 1 rows containing non-finite values (stat_density).
Reduced Dataset
Data Summaries
#melt data frame by Dreissenids and Info_type so we can summarize all our variables of interest in one code chunk
redCSLAP2<-redCSLAP[, c(8, 13, 17, 21, 22, 28, 30, 32, 36, 37, 38, 50:54)]
redmeltCSLAP <- melt(redCSLAP2, id=c("Dreissenids", "Info_Type"), na.rm=TRUE)
#summarize each variable split by invasion status and info
redVariableSummaries<-ddply(redmeltCSLAP, c("Dreissenids", "Info_Type", "variable"), summarize,
Mean = mean(value, na.rm=TRUE),
Median = median(value, na.rm=TRUE),
Min = min(value, na.rm=TRUE),
Max = max(value, na.rm=TRUE),
SD = sd(value, na.rm=TRUE))
redVariableSummaries## Dreissenids Info_Type variable Mean Median Min
## 1 Invaded BS TP 3.945294e-02 0.019850 0.0000000
## 2 Invaded BS Temp_Water 1.511196e+01 15.000000 5.0000000
## 3 Invaded BS CA.SA 2.788857e+01 6.507353 4.4873502
## 4 Invaded BS Mean_Depth_m 6.232740e+00 6.200000 3.7000000
## 5 Invaded BS Percent_Ag 2.046619e+01 20.000000 1.0000000
## 6 Invaded OW Secchi_Depth 4.536559e+00 4.400000 1.1000000
## 7 Invaded OW TP 1.276286e-02 0.011000 0.0036000
## 8 Invaded OW Temp_Water 2.330388e+01 24.000000 10.0000000
## 9 Invaded OW TN 5.232666e-01 0.368810 0.0000000
## 10 Invaded OW True_Color 1.295292e+01 11.000000 0.5000000
## 11 Invaded OW Calcium 2.842750e+01 24.450000 14.3000000
## 12 Invaded OW Extracted_Chl.a 2.372222e+00 1.900000 0.0500000
## 13 Invaded OW ESF_FP_Chl.a 1.945223e+00 1.600000 0.0000000
## 14 Invaded OW ESF_FP_BGA 4.813312e-01 0.100000 0.0000000
## 15 Invaded OW ESF_Microcystin 6.437536e-01 0.440000 0.3285191
## 16 Invaded OW TN_TP 4.676542e+01 31.182701 0.0000000
## 17 Invaded OW CA.SA 2.823056e+01 10.780370 4.4873502
## 18 Invaded OW Mean_Depth_m 6.233231e+00 6.200000 3.7000000
## 19 Invaded OW Percent_Ag 1.934462e+01 20.000000 1.0000000
## 20 Invaded SB Secchi_Depth 2.750000e+00 2.750000 2.7500000
## 21 Invaded SB ESF_FP_Chl.a 2.138201e+03 142.635000 0.2000000
## 22 Invaded SB ESF_FP_BGA 2.002831e+03 85.475000 0.0000000
## 23 Invaded SB ESF_Microcystin 7.493377e+01 43.840108 0.8346926
## 24 Invaded SB CA.SA 1.424389e+01 4.807692 4.6535893
## 25 Invaded SB Mean_Depth_m 5.713333e+00 7.100000 3.7000000
## 26 Invaded SB Percent_Ag 3.256667e+01 21.500000 20.0000000
## 27 Uninvaded BS Secchi_Depth 5.100000e+00 5.100000 5.1000000
## 28 Uninvaded BS TP 3.269729e-02 0.012600 0.0005000
## 29 Uninvaded BS Temp_Water 1.499686e+01 16.000000 4.0000000
## 30 Uninvaded BS TN 3.730000e-01 0.373000 0.3730000
## 31 Uninvaded BS True_Color 1.700000e+01 17.000000 17.0000000
## 32 Uninvaded BS Extracted_Chl.a 1.600000e+00 1.600000 1.6000000
## 33 Uninvaded BS ESF_FP_Chl.a 6.300000e-01 0.630000 0.6300000
## 34 Uninvaded BS ESF_FP_BGA 0.000000e+00 0.000000 0.0000000
## 35 Uninvaded BS TN_TP 8.197802e+00 8.197802 8.1978022
## 36 Uninvaded BS CA.SA 4.077164e+01 12.692967 6.5073529
## 37 Uninvaded BS Mean_Depth_m 8.546602e+00 8.500000 4.2000000
## 38 Uninvaded BS Percent_Ag 1.929450e+01 18.000000 0.0000000
## 39 Uninvaded OW Secchi_Depth 4.136219e+00 4.000000 1.0000000
## 40 Uninvaded OW TP 1.215861e-02 0.009600 0.0033000
## 41 Uninvaded OW Temp_Water 2.320556e+01 23.000000 13.0000000
## 42 Uninvaded OW TN 3.543052e-01 0.331000 0.1080000
## 43 Uninvaded OW True_Color 1.423788e+01 13.000000 0.5000000
## 44 Uninvaded OW Calcium 1.189190e+01 7.700000 1.5300000
## 45 Uninvaded OW Extracted_Chl.a 3.309422e+00 2.300000 0.0500000
## 46 Uninvaded OW ESF_FP_Chl.a 2.736372e+00 1.610000 0.0000000
## 47 Uninvaded OW ESF_FP_BGA 1.112477e+00 0.110000 0.0000000
## 48 Uninvaded OW ESF_Microcystin 5.172282e-01 0.420000 0.2044636
## 49 Uninvaded OW TN_TP 3.727631e+01 32.944785 5.2858958
## 50 Uninvaded OW CA.SA 4.595273e+01 17.211538 6.5073529
## 51 Uninvaded OW Mean_Depth_m 8.728443e+00 8.500000 4.2000000
## 52 Uninvaded OW Percent_Ag 1.678144e+01 18.000000 0.0000000
## 53 Uninvaded SB ESF_FP_Chl.a 1.560474e+03 632.615000 0.2800000
## 54 Uninvaded SB ESF_FP_BGA 9.629969e+02 31.275000 0.0000000
## 55 Uninvaded SB ESF_Microcystin 2.760696e+02 276.069608 276.0696077
## 56 Uninvaded SB CA.SA 1.863356e+01 14.952253 6.5073529
## 57 Uninvaded SB Mean_Depth_m 6.887500e+00 7.550000 4.2000000
## 58 Uninvaded SB Percent_Ag 1.818750e+01 18.000000 1.0000000
## Max SD
## 1 0.374200 5.324365e-02
## 2 25.000000 5.298994e+00
## 3 189.855073 5.436477e+01
## 4 9.900000 1.727590e+00
## 5 48.000000 1.550414e+01
## 6 9.100000 1.395980e+00
## 7 0.066400 7.093941e-03
## 8 36.000000 2.860583e+00
## 9 4.124000 4.399775e-01
## 10 55.000000 9.245752e+00
## 11 57.800000 1.133176e+01
## 12 13.300000 1.839573e+00
## 13 12.150000 1.634573e+00
## 14 10.360000 9.835983e-01
## 15 1.840000 5.345261e-01
## 16 240.595238 4.110511e+01
## 17 189.855073 5.391221e+01
## 18 9.900000 1.697457e+00
## 19 48.000000 1.529962e+01
## 20 2.750000 NA
## 21 31507.500000 6.226951e+03
## 22 31507.500000 6.221888e+03
## 23 361.241045 9.244751e+01
## 24 189.855073 4.064373e+01
## 25 9.900000 1.901513e+00
## 26 48.000000 1.359881e+01
## 27 5.100000 NA
## 28 0.392400 5.827662e-02
## 29 29.000000 6.516206e+00
## 30 0.373000 NA
## 31 17.000000 NA
## 32 1.600000 NA
## 33 0.630000 NA
## 34 0.000000 NA
## 35 8.197802 NA
## 36 210.424710 6.629667e+01
## 37 21.300000 5.113367e+00
## 38 44.000000 1.692249e+01
## 39 8.700000 1.468001e+00
## 40 0.099200 1.067758e-02
## 41 33.000000 3.347532e+00
## 42 1.239000 1.536240e-01
## 43 59.000000 8.816877e+00
## 44 58.300000 1.279472e+01
## 45 24.600000 3.659556e+00
## 46 57.040000 5.021106e+00
## 47 31.020000 3.079598e+00
## 48 1.779509 3.929021e-01
## 49 112.636364 1.959529e+01
## 50 210.424710 7.014974e+01
## 51 21.300000 5.361541e+00
## 52 44.000000 1.561964e+01
## 53 8512.500000 2.245858e+03
## 54 8512.500000 2.253475e+03
## 55 276.069608 NA
## 56 31.521739 1.079730e+01
## 57 8.900000 1.949658e+00
## 58 41.000000 8.166752e+00#Write this dataframe to a csv so the values can be easily put into a word document
write.csv(redVariableSummaries, "./output_data/redVariableSummaries-June2020.csv")#melt Lake List data frame by Dreissenids so we can summarize all our variables of interest in one code chunk
redmeltLakes <- melt(df_match[,c(1:4,6)], id=c("LakeID", "Dreissenids"), na.rm=TRUE)
#summarize each variable split by invasion status and info
redLakeSummaries<-ddply(redmeltLakes, c("Dreissenids", "variable"), summarize,
Mean = mean(value, na.rm=TRUE),
Median = median(value, na.rm=TRUE),
Min = min(value, na.rm=TRUE),
Max = max(value, na.rm=TRUE),
SD = sd(value, na.rm=TRUE))
redLakeSummaries## Dreissenids variable Mean Median Min Max SD
## 1 0 CA.SA 40.38083 14.952253 7.473684 210.4247 69.170089
## 2 0 Mean_Depth_m 8.48750 7.300000 4.200000 21.3000 5.521500
## 3 0 Percent_Ag 17.25000 16.000000 0.000000 44.0000 17.556033
## 4 1 CA.SA 32.57583 8.643862 4.487350 189.8551 63.897069
## 5 1 Mean_Depth_m 6.32500 6.400000 3.700000 9.9000 1.918146
## 6 1 Percent_Ag 21.00000 20.000000 1.000000 48.0000 15.874508#Write this dataframe to a csv so the values can be easily put into a word document
write.csv(redLakeSummaries, "./output_data/redLakeSummaries.csv")Histograms
First we will split our dataset based on information type
redOWCSLAP<-redCSLAP[redCSLAP$Info_Type == "OW",]
redBSCSLAP<-redCSLAP[redCSLAP$Info_Type == "BS",]
redSBCSLAP<-redCSLAP[redCSLAP$Info_Type == "SB",]Open Water TP
ggplot(redOWCSLAP, aes(x=TP)) +
geom_histogram(position="identity", color="black", fill="white")+
facet_wrap(~Dreissenids)+
geom_density()+
theme_minimal()+
labs(title="Open water TP", x="TP (mg/L)", y="Frequency")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 13 rows containing non-finite values (stat_bin).## Warning: Removed 13 rows containing non-finite values (stat_density).
Open Water TN
ggplot(redOWCSLAP, aes(x=TN)) +
geom_histogram(position="identity", color="black", fill="white")+
facet_wrap(~Dreissenids)+
geom_density()+
theme_minimal()+
labs(title="Open water TN", x="TN (mg/L)", y="Frequency")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 20 rows containing non-finite values (stat_bin).## Warning: Removed 20 rows containing non-finite values (stat_density).
Open Water TN:TP
ggplot(redOWCSLAP, aes(x=TN_TP)) +
geom_histogram(position="identity", color="black", fill="white")+
facet_wrap(~Dreissenids)+
geom_density()+
theme_minimal()+
labs(title="Open water TN:TP", x="TN:TP", y="Frequency")+
scale_x_continuous(limits=c(0,100))## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 67 rows containing non-finite values (stat_bin).## Warning: Removed 67 rows containing non-finite values (stat_density).## Warning: Removed 4 rows containing missing values (geom_bar).
Open Water Chlorophyll-a
ggplot(redOWCSLAP, aes(x=Extracted_Chl.a)) +
geom_histogram(position="identity", color="black", fill="white")+
facet_wrap(~Dreissenids)+
geom_density()+
theme_minimal()+
labs(title="Open water chlorophyll a", x="Chlorophyll a (ug/L)", y="Frequency")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 24 rows containing non-finite values (stat_bin).## Warning: Removed 24 rows containing non-finite values (stat_density).
Secchi Depth
ggplot(redOWCSLAP, aes(x=Secchi_Depth)) +
geom_histogram(position="identity", color="black", fill="white")+
facet_wrap(~Dreissenids)+
geom_density()+
theme_minimal()+
labs(title="Secchi Depth", x="Secchi depth (m)", y="Frequency")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 30 rows containing non-finite values (stat_bin).## Warning: Removed 30 rows containing non-finite values (stat_density).
True Color
ggplot(redOWCSLAP, aes(x=True_Color)) +
geom_histogram(position="identity", color="black", fill="white")+
facet_wrap(~Dreissenids)+
geom_density()+
theme_minimal()+
labs(title="True Color", x="True color (PTU)", y="Frequency")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 21 rows containing non-finite values (stat_bin).## Warning: Removed 21 rows containing non-finite values (stat_density).
Shoreline Bloom Chlorophyll
ggplot(redSBCSLAP, aes(x=ESF_FP_Chl.a)) +
geom_histogram(position="identity", color="black", fill="white")+
facet_wrap(~Dreissenids)+
geom_density()+
theme_minimal()+
labs(title="Shoreline Bloom Chlorophyll a", x="Chlorophyll a (ug/L)", y="Frequency")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Shoreline Bloom Microcystin
ggplot(redSBCSLAP, aes(x=ESF_Microcystin)) +
geom_histogram(position="identity", color="black", fill="white")+
facet_wrap(~Dreissenids)+
geom_density()+
theme_minimal()+
labs(title="Shoreline Bloom Microcystin", x="Microcystin (ug/L)", y="Frequency")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 55 rows containing non-finite values (stat_bin).## Warning: Removed 55 rows containing non-finite values (stat_density).## Warning: Groups with fewer than two data points have been dropped.
Shoreline Bloom Microcystin
ggplot(redOWCSLAP, aes(x=ESF_Microcystin)) +
geom_histogram(position="identity", color="black", fill="white")+
facet_wrap(~Dreissenids)+
geom_density()+
theme_minimal()+
labs(title="Open Water Microcystin", x="Microcystin (ug/L)", y="Frequency")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 633 rows containing non-finite values (stat_bin).## Warning: Removed 633 rows containing non-finite values (stat_density).
Historical Analyses for Invaded Lakes
Secchi Depth
Because of the way the data were given to me, Secchi depth has to be done first on its own
hisCSLAP<-read.csv("./historical_data/Historical_CSLAP.csv", na.strings=c("", " ", "ND", "NA", "no filter", "nofilter"))
#extract sample year and month
hisCSLAP$Sample_Year<-as.numeric(format(as.Date(hisCSLAP$Sample_Date, format="%m/%d/%Y"),"%Y"))
hisCSLAP$Sample_Month<-as.numeric(format(as.Date(hisCSLAP$Sample_Date, format="%m/%d/%Y"),"%m"))
#read in invasion year information
Invasion_Year<-read.csv("./raw_data/InvasionYear.csv", header=TRUE, colClasses=c("factor", "numeric"))
#merge with hisCSLAP
hisCSLAP<-merge(hisCSLAP, Invasion_Year, all.x=TRUE)
#create new column `Years_Invaded` by subtracting `Invasion Year` from `Sample Year`
hisCSLAP$Years_Invaded<-hisCSLAP$Sample_Year-hisCSLAP$Invasion_Year
#turn `Sample_Year`, `Sample_Month`, and `Invasion_Year` back to factors
hisCSLAP$Sample_Year<-as.factor(hisCSLAP$Sample_Year)
hisCSLAP$Sample_Month<-as.factor(hisCSLAP$Sample_Month)
hisCSLAP$Invasion_Year<-as.factor(hisCSLAP$Invasion_Year)
#create column with 'Invaded' or "Uninvaded" based on `Years Invaded`
hisCSLAP$Dreissenids<-ifelse(hisCSLAP$Years_Invaded >= 0, "Invaded", "Uninvaded")
hisCSLAP$Dreissenids<-as.factor(hisCSLAP$Dreissenids)#Aggregating yearly means by lake and years invaded
Secchi<-aggregate(Secchi_Depth_m ~ Lake_Name + Years_Invaded, data=hisCSLAP, FUN=mean)
Chl<-aggregate(Extracted_Chl_ug.L ~ Lake_Name + Years_Invaded, data=hisCSLAP, FUN=mean)#t-test pre and post invasion and MEMs
hist(hisCSLAP$Secchi_Depth_m)
t.test(Secchi_Depth_m~Dreissenids, data=hisCSLAP)##
## Welch Two Sample t-test
##
## data: Secchi_Depth_m by Dreissenids
## t = 10.37, df = 1054.5, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.6735570 0.9879379
## sample estimates:
## mean in group Invaded mean in group Uninvaded
## 4.672985 3.842238Secchi.lmer<-lmer(Secchi_Depth_m ~ Dreissenids + (1|LakeID), data=hisCSLAP)
summary(Secchi.lmer)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Secchi_Depth_m ~ Dreissenids + (1 | LakeID)
## Data: hisCSLAP
##
## REML criterion at convergence: 4388.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9324 -0.6854 -0.0512 0.6419 3.5186
##
## Random effects:
## Groups Name Variance Std.Dev.
## LakeID (Intercept) 0.4754 0.6895
## Residual 1.5707 1.2533
## Number of obs: 1324, groups: LakeID, 8
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.62638 0.25053 7.48842 18.466 1.62e-07 ***
## DreissenidsUninvaded -0.70457 0.07522 1320.02303 -9.367 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## DrssndsUnnv -0.175plot(Secchi.lmer)
ggplot(hisCSLAP, aes(x=Dreissenids, y=Secchi_Depth_m))+
geom_boxplot()+
theme_minimal()+
labs(y = "Secchi Depth (m)")## Warning: Removed 779 rows containing non-finite values (stat_boxplot).
#ggsave("historical_Secchi.png")Data Read In For the Rest of the Variables
#historicalCSLAP df edited to match DeRuyter and EatonBrook dfs
hisCSLAP<-read.csv("./historical_data/Historical_CSLAP_edited.csv", na.strings=c("", " ", "ND", "NA", "no filter", "nofilter"))
#DeRuyter and EatonBrook dfs
Eaton<-read.csv("./historical_data/EatonHistoricalClean2.csv", na.strings=c("", " ", "ND", "NA", "no filter", "nofilter"))
Der<-read.csv("./historical_data/DeruyterHistoricalClean2.csv", na.strings=c("", " ", "ND", "NA", "no filter", "nofilter"))
#change levels of Info_Type
hisCSLAP$Info_Type[hisCSLAP$Info_Type == "ow"]<-"OW"
hisCSLAP$Info_Type[hisCSLAP$Info_Type == "sb"]<-"SB"
hisCSLAP$Info_Type<-as.factor(hisCSLAP$Info_Type)
hisCSLAP$Info_Type<-droplevels(hisCSLAP$Info_Type)#Merge hisCSLAP with Deruyter and Eatonbrook
#remove any observations from Eaton or DeRuyter from hisCSLAP
hisCSLAP<-hisCSLAP[hisCSLAP$Lake_Name != "De Ruyter Reservoir",]
hisCSLAP<-hisCSLAP[hisCSLAP$Lake_Name != "Eaton Brook Reservoir",]
#row bind cleaned DeRuyter and EatonBrook
hisCSLAP<-rbind(hisCSLAP, Eaton)
hisCSLAP<-rbind(hisCSLAP, Der)Further organize hisCSLAP, and merge physical characteristics and invasion status
#extract sample year and month
hisCSLAP$Sample_Year<-as.numeric(format(as.Date(hisCSLAP$Sample_Date, format="%m/%d/%Y"),"%Y"))
hisCSLAP$Sample_Month<-as.numeric(format(as.Date(hisCSLAP$Sample_Date, format="%m/%d/%Y"),"%m"))
#merge Invasion Year with hisCSLAP
hisCSLAP<-merge(hisCSLAP, Invasion_Year, all.x=TRUE)
#create new column `Years_Invaded` by subtracting `Invasion Year` from `Sample Year`
hisCSLAP$Years_Invaded<-hisCSLAP$Sample_Year-hisCSLAP$Invasion_Year
#turn `Sample_Year`, `Sample_Month`, and `Invasion_Year` back to factors
hisCSLAP$Sample_Year<-as.factor(hisCSLAP$Sample_Year)
hisCSLAP$Sample_Month<-as.factor(hisCSLAP$Sample_Month)
hisCSLAP$Invasion_Year<-as.factor(hisCSLAP$Invasion_Year)
#create column with 'Invaded' or "Uninvaded" based on `Years Invaded`
hisCSLAP$Dreissenids<-ifelse(hisCSLAP$Years_Invaded >= 0, "Invaded", "Uninvaded")
hisCSLAP$Dreissenids<-as.factor(hisCSLAP$Dreissenids)
#we will also remove and shoreline bloom samples
hisCSLAP<-hisCSLAP[hisCSLAP$Info_Type != "SB",]
hisCSLAP$Info_Type<-droplevels(hisCSLAP$Info_Type)
#create a TN:TP column
hisCSLAP$TN_TP<-hisCSLAP$TN_mg.L/hisCSLAP$TP_mg.L
#finally, create a df with only OW samples and a df with only BS samples
OWhisCSLAP<-hisCSLAP[hisCSLAP$Info_Type == "OW",]
BShisCSLAP<-hisCSLAP[hisCSLAP$Info_Type == "BS",]Aggregating yearly means by lake and years invaded
Chl<-aggregate(Extracted_Chl_ug.L ~ Lake_Name + Years_Invaded, data=hisCSLAP[hisCSLAP$Info_Type == "OW",], FUN=mean)
OWTP<-aggregate(TP_mg.L ~ Lake_Name + Lake_Name + Years_Invaded, data=hisCSLAP[hisCSLAP$Info_Type == "OW",], FUN=mean)
BSTP<-aggregate(TP_mg.L ~ Lake_Name + Lake_Name + Years_Invaded, data=hisCSLAP[hisCSLAP$Info_Type == "BS",], FUN=mean)
OWTN<-aggregate(TN_mg.L ~ Lake_Name + Lake_Name + Years_Invaded, data=hisCSLAP[hisCSLAP$Info_Type == "OW",], FUN=mean)
OWTN_TP<-aggregate(TN_TP ~ Lake_Name + Lake_Name + Years_Invaded, data=hisCSLAP[hisCSLAP$Info_Type == "OW",], FUN=mean)
TC<-aggregate(True_Color_PTU ~ Lake_Name + Years_Invaded, data=hisCSLAP[hisCSLAP$Info_Type == "OW",], FUN=mean)
Calc<-aggregate(Calcium_mg.L ~ Lake_Name + Years_Invaded, data=hisCSLAP[hisCSLAP$Info_Type == "OW",], FUN=mean)How do lakes change after invasion? (t-test for pre and post invasion, then MEMs)
Extracted Chlorophyll-a
hist(log(OWhisCSLAP$Extracted_Chl_ug.L))
t.test(Extracted_Chl_ug.L ~ Dreissenids, data=hisCSLAP[hisCSLAP$Info_Type == "OW",])##
## Welch Two Sample t-test
##
## data: Extracted_Chl_ug.L by Dreissenids
## t = -12.443, df = 1320.3, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -2.624423 -1.909601
## sample estimates:
## mean in group Invaded mean in group Uninvaded
## 2.492524 4.759536Chl.lmer<-lmer(log(Extracted_Chl_ug.L) ~ Dreissenids + (1|LakeID), data=hisCSLAP[hisCSLAP$Info_Type == "OW",])
summary(Chl.lmer)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(Extracted_Chl_ug.L) ~ Dreissenids + (1 | LakeID)
## Data: hisCSLAP[hisCSLAP$Info_Type == "OW", ]
##
## REML criterion at convergence: 3050.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.0999 -0.5200 -0.0071 0.5971 4.1661
##
## Random effects:
## Groups Name Variance Std.Dev.
## LakeID (Intercept) 0.04857 0.2204
## Residual 0.57543 0.7586
## Number of obs: 1323, groups: LakeID, 8
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.034e-01 8.519e-02 7.636e+00 7.083 0.00013 ***
## DreissenidsUninvaded 6.015e-01 4.518e-02 1.318e+03 13.312 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## DrssndsUnnv -0.306plot(Chl.lmer)
Open Water TP
hist(log(OWhisCSLAP$TP_mg.L))
t.test(TP_mg.L ~ Dreissenids, data=hisCSLAP[hisCSLAP$Info_Type == "OW",])##
## Welch Two Sample t-test
##
## data: TP_mg.L by Dreissenids
## t = 3.1251, df = 760.3, p-value = 0.001845
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.0003861491 0.0016908695
## sample estimates:
## mean in group Invaded mean in group Uninvaded
## 0.01197581 0.01093730OWTP.lmer<-lmer(log(TP_mg.L) ~ Dreissenids + (1|LakeID), data=hisCSLAP[hisCSLAP$Info_Type == "OW",])
summary(OWTP.lmer)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(TP_mg.L) ~ Dreissenids + (1 | LakeID)
## Data: hisCSLAP[hisCSLAP$Info_Type == "OW", ]
##
## REML criterion at convergence: 868.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.0407 -0.6192 -0.0512 0.5392 6.2139
##
## Random effects:
## Groups Name Variance Std.Dev.
## LakeID (Intercept) 0.03243 0.1801
## Residual 0.11009 0.3318
## Number of obs: 1315, groups: LakeID, 8
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.48194 0.06539 7.42433 -68.539 1.13e-11 ***
## DreissenidsUninvaded -0.11493 0.01972 1310.85322 -5.827 7.10e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## DrssndsUnnv -0.171plot(OWTP.lmer)
Bottom Sample TP
hist(log(BShisCSLAP$TP_mg.L))
t.test(TP_mg.L ~ Dreissenids, data=hisCSLAP[hisCSLAP$Info_Type == "BS",])##
## Welch Two Sample t-test
##
## data: TP_mg.L by Dreissenids
## t = -2.6076, df = 243.03, p-value = 0.009682
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.08319781 -0.01159341
## sample estimates:
## mean in group Invaded mean in group Uninvaded
## 0.04436469 0.09176031BSTP.lmer<-lmer(log(TP_mg.L) ~ Dreissenids + (1|LakeID), data=hisCSLAP[hisCSLAP$Info_Type == "BS",])
summary(BSTP.lmer)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(TP_mg.L) ~ Dreissenids + (1 | LakeID)
## Data: hisCSLAP[hisCSLAP$Info_Type == "BS", ]
##
## REML criterion at convergence: 1778.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4685 -0.5772 -0.1504 0.5627 4.1494
##
## Random effects:
## Groups Name Variance Std.Dev.
## LakeID (Intercept) 0.4817 0.6941
## Residual 0.8566 0.9255
## Number of obs: 651, groups: LakeID, 8
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -3.78692 0.24993 7.12997 -15.152 1.1e-06 ***
## DreissenidsUninvaded 0.33838 0.08942 647.96512 3.784 0.000169 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## DrssndsUnnv -0.106plot(BSTP.lmer)
Open Water TN
hist(log(OWhisCSLAP$TN_mg.L))
t.test(TN_mg.L ~ Dreissenids, data=hisCSLAP[hisCSLAP$Info_Type == "OW",])##
## Welch Two Sample t-test
##
## data: TN_mg.L by Dreissenids
## t = -0.82352, df = 240.57, p-value = 0.411
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.12748917 0.05231878
## sample estimates:
## mean in group Invaded mean in group Uninvaded
## 0.4895265 0.5271117OWTN.lmer<-lmer(log(TN_mg.L) ~ Dreissenids + (1|LakeID), data=hisCSLAP[hisCSLAP$Info_Type == "OW",])
summary(OWTN.lmer)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(TN_mg.L) ~ Dreissenids + (1 | LakeID)
## Data: hisCSLAP[hisCSLAP$Info_Type == "OW", ]
##
## REML criterion at convergence: 880.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.3884 -0.5927 -0.0039 0.5499 5.6278
##
## Random effects:
## Groups Name Variance Std.Dev.
## LakeID (Intercept) 0.2847 0.5336
## Residual 0.2084 0.4565
## Number of obs: 661, groups: LakeID, 8
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.855286 0.189889 7.030046 -4.504 0.00275 **
## DreissenidsUninvaded 0.004495 0.048195 655.931431 0.093 0.92572
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## DrssndsUnnv -0.060Open Water TN:TP
hist(log(OWhisCSLAP$TN_TP))
t.test(TN_TP ~ Dreissenids, data=hisCSLAP[hisCSLAP$Info_Type == "OW",])##
## Welch Two Sample t-test
##
## data: TN_TP by Dreissenids
## t = -0.65903, df = 251.56, p-value = 0.5105
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -10.553469 5.261396
## sample estimates:
## mean in group Invaded mean in group Uninvaded
## 45.67168 48.31771OWTN_TP.lmer<-lmer(log(TN_TP) ~ Dreissenids + (1|LakeID), data=hisCSLAP[hisCSLAP$Info_Type == "OW",])
summary(OWTN_TP.lmer)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(TN_TP) ~ Dreissenids + (1 | LakeID)
## Data: hisCSLAP[hisCSLAP$Info_Type == "OW", ]
##
## REML criterion at convergence: 1073.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2261 -0.5569 -0.0289 0.5560 4.2435
##
## Random effects:
## Groups Name Variance Std.Dev.
## LakeID (Intercept) 0.2491 0.4991
## Residual 0.2877 0.5364
## Number of obs: 651, groups: LakeID, 8
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.62840 0.17831 7.05445 20.35 1.59e-07 ***
## DreissenidsUninvaded 0.06740 0.05665 647.39946 1.19 0.235
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## DrssndsUnnv -0.075plot(OWTN_TP.lmer)
Open Water True Color
t.test(True_Color_PTU ~ Dreissenids, data=hisCSLAP[hisCSLAP$Info_Type == "OW",])##
## Welch Two Sample t-test
##
## data: True_Color_PTU by Dreissenids
## t = 11.754, df = 1008.7, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 5.747462 8.051237
## sample estimates:
## mean in group Invaded mean in group Uninvaded
## 14.263158 7.363808TC.lmer<-lmer(True_Color_PTU ~ Dreissenids + (1|LakeID), data=hisCSLAP[hisCSLAP$Info_Type == "OW",])
summary(TC.lmer)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: True_Color_PTU ~ Dreissenids + (1 | LakeID)
## Data: hisCSLAP[hisCSLAP$Info_Type == "OW", ]
##
## REML criterion at convergence: 9688.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3027 -0.5143 -0.1901 0.2417 7.1496
##
## Random effects:
## Groups Name Variance Std.Dev.
## LakeID (Intercept) 44.38 6.662
## Residual 85.84 9.265
## Number of obs: 1325, groups: LakeID, 8
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 15.1934 2.3925 7.0424 6.351 0.000376 ***
## DreissenidsUninvaded -5.8055 0.5509 1319.0393 -10.537 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## DrssndsUnnv -0.132Calcium
t.test(Calcium_mg.L ~ Dreissenids, data=hisCSLAP[hisCSLAP$Info_Type == "OW",])##
## Welch Two Sample t-test
##
## data: Calcium_mg.L by Dreissenids
## t = -0.034601, df = 64.648, p-value = 0.9725
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -4.422633 4.272010
## sample estimates:
## mean in group Invaded mean in group Uninvaded
## 30.53299 30.60830Calc.lmer<-lmer(Calcium_mg.L ~ Dreissenids + (1|LakeID), data=hisCSLAP[hisCSLAP$Info_Type == "OW",])
summary(Calc.lmer)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Calcium_mg.L ~ Dreissenids + (1 | LakeID)
## Data: hisCSLAP[hisCSLAP$Info_Type == "OW", ]
##
## REML criterion at convergence: 858.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7693 -0.3958 -0.0114 0.3219 3.5680
##
## Random effects:
## Groups Name Variance Std.Dev.
## LakeID (Intercept) 88.02 9.382
## Residual 43.69 6.610
## Number of obs: 127, groups: LakeID, 8
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 31.031 3.414 7.283 9.090 3.13e-05 ***
## DreissenidsUninvaded 2.383 1.637 122.621 1.455 0.148
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## DrssndsUnnv -0.149plot(Calc.lmer)
Boxplots
Chlorophyll-a
ggplot(hisCSLAP[hisCSLAP$Info_Type == "OW",], aes(x=Dreissenids, y=Extracted_Chl_ug.L)) +
geom_boxplot()+
theme_bw()+
labs(y = "Extracted Chlorophyll-a (mg/L)")## Warning: Removed 54 rows containing non-finite values (stat_boxplot).
#ggsave("./output_figures/historical_Chl.png")OWTP
ggplot(hisCSLAP[hisCSLAP$Info_Type == "OW",], aes(x=Dreissenids, y=TP_mg.L)) +
geom_boxplot()+
theme_bw()+
labs(y = "Open Water TP (mg/L)")## Warning: Removed 62 rows containing non-finite values (stat_boxplot).
#ggsave("./output_figures/historical_OWTP.png")BSTP
ggplot(hisCSLAP[hisCSLAP$Info_Type == "BS",], aes(x=Dreissenids, y=TP_mg.L)) +
geom_boxplot()+
theme_bw()+
labs(y = "Bottom Sample TP (mg/L)")## Warning: Removed 228 rows containing non-finite values (stat_boxplot).
#ggsave("./output_figures/historical_BSTP.png")OWTN
ggplot(hisCSLAP[hisCSLAP$Info_Type == "OW",], aes(x=Dreissenids, y=TN_mg.L)) +
geom_boxplot()+
theme_bw()+
labs(y = "Open Water TN (mg/L)")## Warning: Removed 716 rows containing non-finite values (stat_boxplot).
#ggsave("./output_figures/historical_OWTN.png")OWTN_TP
ggplot(hisCSLAP[hisCSLAP$Info_Type == "OW",], aes(x=Dreissenids, y=TN_TP)) +
geom_boxplot()+
theme_bw()+
labs(y = "Open Water TN:TP")## Warning: Removed 726 rows containing non-finite values (stat_boxplot).
#ggsave("./output_figures/historical_OWTN_TP.png")OW TC
ggplot(hisCSLAP[hisCSLAP$Info_Type == "OW",], aes(x=Dreissenids, y=True_Color_PTU)) +
geom_boxplot()+
theme_bw()+
labs(y = "Open Water True Color")## Warning: Removed 52 rows containing non-finite values (stat_boxplot).
#ggsave("./output_figures/historical_OWTC.png")OW Calcium
ggplot(hisCSLAP[hisCSLAP$Info_Type == "OW",], aes(x=Dreissenids, y=Calcium_mg.L)) +
geom_boxplot()+
theme_bw()+
labs(y = "Open Water Calcium")## Warning: Removed 1250 rows containing non-finite values (stat_boxplot).
#ggsave("./output_figures/historical_OWCalcium.png")Mixed Effects Models
Global Dataset
Open water TN:TP
TNTP<-lmer(log(TN_TP+.01) ~ Dreissenids + log(CA.SA) + log(Mean_Depth_m) + Percent_Ag + (1|Sample_Year) + (1|Sample_Month) + (1|LakeID) + (1|Sample_Year:LakeID), data=OWCSLAP)
summary(TNTP)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(TN_TP + 0.01) ~ Dreissenids + log(CA.SA) + log(Mean_Depth_m) +
## Percent_Ag + (1 | Sample_Year) + (1 | Sample_Month) + (1 |
## LakeID) + (1 | Sample_Year:LakeID)
## Data: OWCSLAP
##
## REML criterion at convergence: 4288.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -14.8325 -0.4551 0.0402 0.4806 9.2136
##
## Random effects:
## Groups Name Variance Std.Dev.
## Sample_Year:LakeID (Intercept) 0.052851 0.22989
## LakeID (Intercept) 0.058346 0.24155
## Sample_Month (Intercept) 0.001713 0.04139
## Sample_Year (Intercept) 0.009578 0.09787
## Residual 0.255404 0.50538
## Number of obs: 2601, groups:
## Sample_Year:LakeID, 342; LakeID, 67; Sample_Month, 6; Sample_Year, 6
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.183775 0.165460 79.439722 19.242 <2e-16 ***
## DreissenidsUninvaded -0.032480 0.105180 92.981532 -0.309 0.7582
## log(CA.SA) 0.048601 0.033665 61.368921 1.444 0.1539
## log(Mean_Depth_m) 0.125403 0.052248 63.715137 2.400 0.0193 *
## Percent_Ag 0.001651 0.002880 66.035094 0.573 0.5683
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) DrssnU l(CA.S l(M_D_
## DrssndsUnnv -0.673
## log(CA.SA) -0.458 0.044
## lg(Mn_Dpt_) -0.520 0.087 -0.040
## Percent_Ag -0.279 0.326 -0.044 -0.053plot(TNTP)
qqPlot(resid(TNTP))
## 1395 4458
## 711 2353Bottom Sample TP
BSTP<-lmer(log(TP+.01) ~ Dreissenids + log(CA.SA) + log(Mean_Depth_m) + Percent_Ag + (1|Sample_Year) + (1|Sample_Month) + (1|LakeID) + (1|Sample_Year:LakeID), data=BSCSLAP)
summary(BSTP)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(TP + 0.01) ~ Dreissenids + log(CA.SA) + log(Mean_Depth_m) +
## Percent_Ag + (1 | Sample_Year) + (1 | Sample_Month) + (1 |
## LakeID) + (1 | Sample_Year:LakeID)
## Data: BSCSLAP
##
## REML criterion at convergence: 3060.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4588 -0.4356 -0.0638 0.3495 4.9671
##
## Random effects:
## Groups Name Variance Std.Dev.
## Sample_Year:LakeID (Intercept) 0.081919 0.28621
## LakeID (Intercept) 0.387229 0.62228
## Sample_Month (Intercept) 0.007195 0.08482
## Sample_Year (Intercept) 0.003081 0.05551
## Residual 0.243055 0.49301
## Number of obs: 1808, groups:
## Sample_Year:LakeID, 269; LakeID, 53; Sample_Month, 6; Sample_Year, 6
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -3.550746 0.448242 60.210119 -7.921 6.52e-11 ***
## DreissenidsUninvaded 0.246827 0.206337 134.468860 1.196 0.23371
## log(CA.SA) -0.013752 0.090666 48.401520 -0.152 0.88007
## log(Mean_Depth_m) -0.145212 0.203496 48.540796 -0.714 0.47890
## Percent_Ag 0.022462 0.006911 50.849848 3.250 0.00205 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) DrssnU l(CA.S l(M_D_
## DrssndsUnnv -0.478
## log(CA.SA) -0.244 0.063
## lg(Mn_Dpt_) -0.740 0.022 -0.252
## Percent_Ag -0.346 0.256 -0.116 0.177plot(BSTP)
qqPlot(resid(BSTP))
## 3945 946
## 1445 355Open water TN
Full model error: Model failed to converge
-Remove Sample_Month. Sample_Year, Sample_Year:LakeID (low variance) to resolve error
TN<-lmer(log(TN+.01) ~ Dreissenids + log(CA.SA) + log(Mean_Depth_m) + Percent_Ag + (1|LakeID), data=OWCSLAP)
summary(TN)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(TN + 0.01) ~ Dreissenids + log(CA.SA) + log(Mean_Depth_m) +
## Percent_Ag + (1 | LakeID)
## Data: OWCSLAP
##
## REML criterion at convergence: 2964
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -8.3622 -0.5006 0.0011 0.4902 8.9046
##
## Random effects:
## Groups Name Variance Std.Dev.
## LakeID (Intercept) 0.06457 0.2541
## Residual 0.16644 0.4080
## Number of obs: 2638, groups: LakeID, 67
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.977263 0.140314 91.572863 -6.965 4.89e-10 ***
## DreissenidsUninvaded -0.075003 0.083028 198.446052 -0.903 0.36744
## log(CA.SA) 0.047215 0.032257 62.385805 1.464 0.14828
## log(Mean_Depth_m) -0.137653 0.049696 63.297223 -2.770 0.00735 **
## Percent_Ag 0.012407 0.002696 67.534806 4.602 1.90e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) DrssnU l(CA.S l(M_D_
## DrssndsUnnv -0.628
## log(CA.SA) -0.512 0.042
## lg(Mn_Dpt_) -0.568 0.071 -0.034
## Percent_Ag -0.238 0.276 -0.053 -0.063plot(TN)
qqPlot(resid(TN))
## 3666 1395
## 1966 711Open water chlorophyll
Chl<-lmer(log(Extracted_Chl.a+.01) ~ Dreissenids + log(CA.SA) + log(Mean_Depth_m) + Percent_Ag + (1|Sample_Year) + (1|Sample_Month) + (1|LakeID) + (1|Sample_Year:LakeID), data=OWCSLAP)
summary(Chl)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## log(Extracted_Chl.a + 0.01) ~ Dreissenids + log(CA.SA) + log(Mean_Depth_m) +
## Percent_Ag + (1 | Sample_Year) + (1 | Sample_Month) + (1 |
## LakeID) + (1 | Sample_Year:LakeID)
## Data: OWCSLAP
##
## REML criterion at convergence: 5949.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.5567 -0.4517 0.0417 0.5127 4.1829
##
## Random effects:
## Groups Name Variance Std.Dev.
## Sample_Year:LakeID (Intercept) 0.05400 0.2324
## LakeID (Intercept) 0.21540 0.4641
## Sample_Month (Intercept) 0.02576 0.1605
## Sample_Year (Intercept) 0.01314 0.1146
## Residual 0.49917 0.7065
## Number of obs: 2591, groups:
## Sample_Year:LakeID, 342; LakeID, 67; Sample_Month, 6; Sample_Year, 6
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.355434 0.282568 85.411626 4.797 6.77e-06 ***
## DreissenidsUninvaded -0.146767 0.167536 127.257977 -0.876 0.38266
## log(CA.SA) 0.037582 0.060147 56.206917 0.625 0.53461
## log(Mean_Depth_m) -0.295017 0.092811 57.298075 -3.179 0.00239 **
## Percent_Ag 0.002679 0.005059 60.535425 0.529 0.59842
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) DrssnU l(CA.S l(M_D_
## DrssndsUnnv -0.628
## log(CA.SA) -0.476 0.042
## lg(Mn_Dpt_) -0.533 0.078 -0.033
## Percent_Ag -0.244 0.294 -0.052 -0.061plot(Chl)
qqPlot(resid(Chl))
## 1989 3530
## 1003 1856Secchi
Secchi<-lmer(log(Secchi_Depth+.01) ~ Dreissenids + log(CA.SA) + log(Mean_Depth_m) + Percent_Ag + (1|Sample_Year) + (1|Sample_Month) + (1|LakeID) + (1|Sample_Year:LakeID), data=OWCSLAP)
summary(Secchi)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## log(Secchi_Depth + 0.01) ~ Dreissenids + log(CA.SA) + log(Mean_Depth_m) +
## Percent_Ag + (1 | Sample_Year) + (1 | Sample_Month) + (1 |
## LakeID) + (1 | Sample_Year:LakeID)
## Data: OWCSLAP
##
## REML criterion at convergence: -311.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.9201 -0.5351 0.0207 0.5662 4.7949
##
## Random effects:
## Groups Name Variance Std.Dev.
## Sample_Year:LakeID (Intercept) 1.150e-02 0.107227
## LakeID (Intercept) 7.080e-02 0.266084
## Sample_Month (Intercept) 4.551e-05 0.006746
## Sample_Year (Intercept) 8.639e-04 0.029393
## Residual 4.079e-02 0.201956
## Number of obs: 2590, groups:
## Sample_Year:LakeID, 342; LakeID, 67; Sample_Month, 6; Sample_Year, 6
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.063472 0.140820 95.144934 7.552 2.60e-11 ***
## DreissenidsUninvaded -0.091870 0.078020 230.031893 -1.178 0.2402
## log(CA.SA) -0.071893 0.033512 62.546322 -2.145 0.0358 *
## log(Mean_Depth_m) 0.336271 0.051584 63.305400 6.519 1.34e-08 ***
## Percent_Ag -0.002311 0.002784 68.034599 -0.830 0.4093
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) DrssnU l(CA.S l(M_D_
## DrssndsUnnv -0.586
## log(CA.SA) -0.523 0.034
## lg(Mn_Dpt_) -0.579 0.064 -0.033
## Percent_Ag -0.212 0.250 -0.059 -0.068plot(Secchi)
qqPlot(resid(Secchi))
## 4582 4641
## 2411 2440Open Water True Color
TC<-lmer(log(True_Color+.01) ~ Dreissenids + log(CA.SA) + log(Mean_Depth_m) + Percent_Ag + (1|Sample_Year) + (1|Sample_Month) + (1|LakeID) + (1|Sample_Year:LakeID), data=OWCSLAP)
summary(TC)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## log(True_Color + 0.01) ~ Dreissenids + log(CA.SA) + log(Mean_Depth_m) +
## Percent_Ag + (1 | Sample_Year) + (1 | Sample_Month) + (1 |
## LakeID) + (1 | Sample_Year:LakeID)
## Data: OWCSLAP
##
## REML criterion at convergence: 2860.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -9.0242 -0.4258 0.0510 0.5180 3.4438
##
## Random effects:
## Groups Name Variance Std.Dev.
## Sample_Year:LakeID (Intercept) 0.06792 0.2606
## LakeID (Intercept) 0.18192 0.4265
## Sample_Month (Intercept) 0.01635 0.1279
## Sample_Year (Intercept) 0.07901 0.2811
## Residual 0.13060 0.3614
## Number of obs: 2612, groups:
## Sample_Year:LakeID, 343; LakeID, 67; Sample_Month, 6; Sample_Year, 6
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.179e+00 2.738e-01 7.195e+01 7.957 1.85e-11 ***
## DreissenidsUninvaded 3.631e-01 1.480e-01 1.507e+02 2.453 0.01532 *
## log(CA.SA) 1.552e-01 5.483e-02 6.103e+01 2.830 0.00629 **
## log(Mean_Depth_m) -2.406e-01 8.455e-02 6.207e+01 -2.846 0.00599 **
## Percent_Ag -2.579e-04 4.599e-03 6.587e+01 -0.056 0.95545
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) DrssnU l(CA.S l(M_D_
## DrssndsUnnv -0.572
## log(CA.SA) -0.446 0.039
## lg(Mn_Dpt_) -0.498 0.074 -0.033
## Percent_Ag -0.218 0.285 -0.054 -0.063plot(TC)
qqPlot(resid(TC))
## 3656 4774
## 1940 2531Shoreline Bloom Chlorophyll a
Full model error: boundary (singular) fit and model failed to converge
Remove Sample_Year, Sample_Month, and Sample_Year:LakeID (low variance) to resolve
SBChl<-lmer(log(ESF_FP_Chl.a+.01) ~ Dreissenids + log(CA.SA) + log(Mean_Depth_m) + Percent_Ag + (1|LakeID), data=SBCSLAP)
summary(SBChl)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## log(ESF_FP_Chl.a + 0.01) ~ Dreissenids + log(CA.SA) + log(Mean_Depth_m) +
## Percent_Ag + (1 | LakeID)
## Data: SBCSLAP
##
## REML criterion at convergence: 990.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.96703 -0.56007 0.06596 0.62799 2.26269
##
## Random effects:
## Groups Name Variance Std.Dev.
## LakeID (Intercept) 2.293 1.514
## Residual 4.428 2.104
## Number of obs: 219, groups: LakeID, 42
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.12907 1.56564 35.94401 3.276 0.00234 **
## DreissenidsUninvaded -0.21254 0.98272 46.03089 -0.216 0.82973
## log(CA.SA) 0.26531 0.33747 35.26516 0.786 0.43701
## log(Mean_Depth_m) -0.42431 0.50696 28.67188 -0.837 0.40953
## Percent_Ag 0.03699 0.02638 35.94149 1.402 0.16953
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) DrssnU l(CA.S l(M_D_
## DrssndsUnnv -0.710
## log(CA.SA) -0.503 0.070
## lg(Mn_Dpt_) -0.544 0.071 0.058
## Percent_Ag -0.392 0.501 -0.077 -0.090plot(SBChl)
qqPlot(resid(SBChl))
## 614 4310
## 44 195Shoreline Bloom Microcystin
We want to add TN:TP as a predictor (fixed effect), but these variables aren’t collected with shoreline bloom data. So we will instead use the yearly average for a lake.
#Extracting average annual TN_TP for each lake
library(plyr)
avgTNTP<-ddply(OWCSLAP, c("LakeID", "Sample_Year"), summarize,
Mean = mean(TN_TP, na.rm=TRUE))
colnames(avgTNTP)[colnames(avgTNTP)=="Mean"] <- "TN_TP"
#Merge these values to the SBCSLAP df
SBCSLAP<-SBCSLAP[,c(1:49, 51:54)]
SBCSLAP<-merge(SBCSLAP, avgTNTP, by=c("LakeID", "Sample_Year"), all.x=TRUE, all.y=FALSE)Full model error: boundary (singular) fit
Remove Sample_Month and Sample_Year:LakeID to resolve
SBmicro<-lmer(log(ESF_Microcystin) ~ Dreissenids + TN_TP + log(CA.SA) + Mean_Depth_m + Percent_Ag + (1|Sample_Year) + (1|LakeID), data=SBCSLAP)
summary(SBmicro)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## log(ESF_Microcystin) ~ Dreissenids + TN_TP + log(CA.SA) + Mean_Depth_m +
## Percent_Ag + (1 | Sample_Year) + (1 | LakeID)
## Data: SBCSLAP
##
## REML criterion at convergence: 176.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.28513 -0.53162 0.06219 0.53296 1.59305
##
## Random effects:
## Groups Name Variance Std.Dev.
## LakeID (Intercept) 1.846 1.359
## Sample_Year (Intercept) 5.793 2.407
## Residual 2.281 1.510
## Number of obs: 43, groups: LakeID, 7; Sample_Year, 6
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 16.56817 6.64246 1.83807 2.494 0.141
## DreissenidsUninvaded -1.70297 2.24234 0.83298 -0.759 0.606
## TN_TP -0.02691 0.02376 5.71424 -1.133 0.303
## log(CA.SA) -3.02465 2.17918 1.75354 -1.388 0.315
## Mean_Depth_m -0.66268 0.44983 1.60063 -1.473 0.307
## Percent_Ag -0.06315 0.06490 1.31865 -0.973 0.475
##
## Correlation of Fixed Effects:
## (Intr) DrssnU TN_TP l(CA.S Mn_Dp_
## DrssndsUnnv -0.285
## TN_TP -0.031 -0.049
## log(CA.SA) -0.850 -0.154 -0.040
## Mean_Dpth_m -0.745 0.169 -0.283 0.583
## Percent_Ag -0.694 0.596 -0.245 0.415 0.506plot(SBmicro)
qqPlot(resid(SBmicro))
## 60 36
## 23 16Open Water Microcystin
OWmicro<-lmer(log(ESF_Microcystin) ~ Dreissenids + TN_TP + log(CA.SA) + Mean_Depth_m + Percent_Ag + (1|Sample_Year) + (1|LakeID) + (1|Sample_Month) + (1|Sample_Year:LakeID), data=OWCSLAP)
summary(OWmicro)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## log(ESF_Microcystin) ~ Dreissenids + TN_TP + log(CA.SA) + Mean_Depth_m +
## Percent_Ag + (1 | Sample_Year) + (1 | LakeID) + (1 | Sample_Month) +
## (1 | Sample_Year:LakeID)
## Data: OWCSLAP
##
## REML criterion at convergence: 198.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.3744 -0.4953 -0.1004 0.3330 2.6445
##
## Random effects:
## Groups Name Variance Std.Dev.
## Sample_Year:LakeID (Intercept) 0.14350 0.37882
## LakeID (Intercept) 0.00265 0.05148
## Sample_Month (Intercept) 0.02637 0.16239
## Sample_Year (Intercept) 5.31141 2.30465
## Residual 0.15525 0.39402
## Number of obs: 99, groups:
## Sample_Year:LakeID, 68; LakeID, 50; Sample_Month, 5; Sample_Year, 5
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.249822 1.093728 4.539615 0.228 0.8293
## DreissenidsUninvaded 0.101416 0.222614 38.515454 0.456 0.6513
## TN_TP -0.006788 0.003091 74.105964 -2.196 0.0312 *
## log(CA.SA) -0.128848 0.082681 42.715034 -1.558 0.1265
## Mean_Depth_m -0.023865 0.023937 34.741718 -0.997 0.3257
## Percent_Ag 0.010934 0.006191 39.154276 1.766 0.0852 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) DrssnU TN_TP l(CA.S Mn_Dp_
## DrssndsUnnv -0.193
## TN_TP -0.088 -0.013
## log(CA.SA) -0.130 -0.178 0.111
## Mean_Dpth_m -0.124 0.144 -0.078 0.092
## Percent_Ag -0.083 0.504 -0.165 -0.286 0.004plot(OWmicro)
qqPlot(resid(OWmicro))
## 2537 115
## 63 2Reduced Dataset
Open water TN:TP
redTNTP<-lmer(log(TN_TP+.01) ~ Dreissenids + log(CA.SA) + log(Mean_Depth_m) + Percent_Ag + (1|Sample_Year) + (1|Sample_Month) + (1|LakeID) + (1|Sample_Year:LakeID), data=redOWCSLAP)
summary(redTNTP)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(TN_TP + 0.01) ~ Dreissenids + log(CA.SA) + log(Mean_Depth_m) +
## Percent_Ag + (1 | Sample_Year) + (1 | Sample_Month) + (1 |
## LakeID) + (1 | Sample_Year:LakeID)
## Data: redOWCSLAP
##
## REML criterion at convergence: 1050.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -14.6683 -0.3822 0.0591 0.4478 4.3339
##
## Random effects:
## Groups Name Variance Std.Dev.
## Sample_Year:LakeID (Intercept) 5.118e-02 0.226226
## LakeID (Intercept) 6.634e-02 0.257560
## Sample_Month (Intercept) 2.367e-05 0.004865
## Sample_Year (Intercept) 3.632e-03 0.060263
## Residual 2.563e-01 0.506264
## Number of obs: 631, groups:
## Sample_Year:LakeID, 82; LakeID, 16; Sample_Month, 6; Sample_Year, 6
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.916916 0.388877 11.345800 7.501 1e-05 ***
## DreissenidsUninvaded -0.192372 0.132153 22.573705 -1.456 0.159248
## log(CA.SA) 0.376821 0.082511 11.462582 4.567 0.000727 ***
## log(Mean_Depth_m) -0.269620 0.226359 11.275287 -1.191 0.258079
## Percent_Ag 0.009508 0.004822 11.790439 1.972 0.072581 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) DrssnU l(CA.S l(M_D_
## DrssndsUnnv 0.013
## log(CA.SA) 0.052 -0.107
## lg(Mn_Dpt_) -0.794 -0.117 -0.575
## Percent_Ag -0.482 0.009 0.083 0.178plot(redTNTP)
qqPlot(resid(redTNTP))
## 359 866
## 152 401Bottom Sample TP
redBSTP<-lmer(log(TP+.01) ~ Dreissenids + log(CA.SA) + log(Mean_Depth_m) + Percent_Ag + (1|Sample_Year) + (1|Sample_Month) + (1|LakeID) + (1|Sample_Year:LakeID), data=redBSCSLAP)
summary(redBSTP)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(TP + 0.01) ~ Dreissenids + log(CA.SA) + log(Mean_Depth_m) +
## Percent_Ag + (1 | Sample_Year) + (1 | Sample_Month) + (1 |
## LakeID) + (1 | Sample_Year:LakeID)
## Data: redBSCSLAP
##
## REML criterion at convergence: 941
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9250 -0.5174 -0.0940 0.3573 4.1989
##
## Random effects:
## Groups Name Variance Std.Dev.
## Sample_Year:LakeID (Intercept) 0.086182 0.29357
## LakeID (Intercept) 0.085774 0.29287
## Sample_Month (Intercept) 0.013852 0.11769
## Sample_Year (Intercept) 0.003924 0.06265
## Residual 0.236124 0.48593
## Number of obs: 567, groups:
## Sample_Year:LakeID, 86; LakeID, 16; Sample_Month, 6; Sample_Year, 6
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -3.35180 0.45027 10.98550 -7.444 1.3e-05 ***
## DreissenidsUninvaded -0.04116 0.15608 18.23044 -0.264 0.79496
## log(CA.SA) -0.31308 0.09501 10.84399 -3.295 0.00727 **
## log(Mean_Depth_m) 0.31715 0.26053 10.63704 1.217 0.24980
## Percent_Ag 0.00985 0.00547 10.55784 1.801 0.10037
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) DrssnU l(CA.S l(M_D_
## DrssndsUnnv 0.016
## log(CA.SA) 0.047 -0.098
## lg(Mn_Dpt_) -0.787 -0.127 -0.573
## Percent_Ag -0.478 -0.001 0.095 0.173plot(redBSTP)
qqPlot(resid(redBSTP))
## 930 870
## 393 367Open Water Chlorophyll-a
redChl<-lmer(log(Extracted_Chl.a+1) ~ Dreissenids + log(CA.SA) + log(Mean_Depth_m) + Percent_Ag + (1|LakeID) + (1|Sample_Year) + (1|Sample_Month) + (1|Sample_Year:LakeID), data=redOWCSLAP)
summary(redChl)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## log(Extracted_Chl.a + 1) ~ Dreissenids + log(CA.SA) + log(Mean_Depth_m) +
## Percent_Ag + (1 | LakeID) + (1 | Sample_Year) + (1 | Sample_Month) +
## (1 | Sample_Year:LakeID)
## Data: redOWCSLAP
##
## REML criterion at convergence: 898
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6966 -0.6137 -0.0092 0.5544 3.7621
##
## Random effects:
## Groups Name Variance Std.Dev.
## Sample_Year:LakeID (Intercept) 0.0249526 0.1580
## LakeID (Intercept) 0.0501381 0.2239
## Sample_Month (Intercept) 0.0109024 0.1044
## Sample_Year (Intercept) 0.0008352 0.0289
## Residual 0.2031186 0.4507
## Number of obs: 635, groups:
## Sample_Year:LakeID, 83; LakeID, 16; Sample_Month, 6; Sample_Year, 6
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.923987 0.334299 9.114526 2.764 0.0217 *
## DreissenidsUninvaded -0.017117 0.108678 20.683029 -0.158 0.8764
## log(CA.SA) -0.140648 0.070274 8.889603 -2.001 0.0768 .
## log(Mean_Depth_m) 0.379757 0.192944 8.787069 1.968 0.0813 .
## Percent_Ag -0.002600 0.004095 9.045667 -0.635 0.5412
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) DrssnU l(CA.S l(M_D_
## DrssndsUnnv 0.014
## log(CA.SA) 0.051 -0.102
## lg(Mn_Dpt_) -0.788 -0.115 -0.575
## Percent_Ag -0.476 0.007 0.084 0.177plot(redChl)
qqPlot(resid(redChl))
## 433 161
## 199 69Open Water Secchi Depth
-Sample_Year and Sample_Month removed for having low variance
redSecchi <- lmer(log(Secchi_Depth) ~ Dreissenids + log(CA.SA) + log(Mean_Depth_m) + Percent_Ag + (1|LakeID)+ (1|Sample_Year) + (1|Sample_Month) + (1|Sample_Year:LakeID), data=redOWCSLAP)
summary(redSecchi)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(Secchi_Depth) ~ Dreissenids + log(CA.SA) + log(Mean_Depth_m) +
## Percent_Ag + (1 | LakeID) + (1 | Sample_Year) + (1 | Sample_Month) +
## (1 | Sample_Year:LakeID)
## Data: redOWCSLAP
##
## REML criterion at convergence: 163.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0709 -0.5099 -0.0049 0.5735 3.9593
##
## Random effects:
## Groups Name Variance Std.Dev.
## Sample_Year:LakeID (Intercept) 0.0220862 0.14861
## LakeID (Intercept) 0.0499813 0.22356
## Sample_Month (Intercept) 0.0002667 0.01633
## Sample_Year (Intercept) 0.0005895 0.02428
## Residual 0.0582525 0.24136
## Number of obs: 629, groups:
## Sample_Year:LakeID, 82; LakeID, 16; Sample_Month, 6; Sample_Year, 6
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.832306 0.319084 11.531942 5.742 0.000108 ***
## DreissenidsUninvaded -0.078643 0.095102 35.721476 -0.827 0.413767
## log(CA.SA) 0.047830 0.067658 11.671969 0.707 0.493483
## log(Mean_Depth_m) -0.234894 0.185840 11.589070 -1.264 0.231077
## Percent_Ag -0.003829 0.003958 11.983623 -0.967 0.352421
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) DrssnU l(CA.S l(M_D_
## DrssndsUnnv 0.014
## log(CA.SA) 0.053 -0.095
## lg(Mn_Dpt_) -0.797 -0.104 -0.578
## Percent_Ag -0.481 0.009 0.082 0.178plot(redSecchi)
qqPlot(resid(redSecchi))
## 959 1261
## 439 595Open Water True Color
Full model error: Model failed to converge
Remove Sample_Year and Sample_Month (low variance) to resolve
redOWTC<-lmer(True_Color ~ Dreissenids + log(CA.SA) + log(Mean_Depth_m) + Percent_Ag + (1|LakeID) + + (1|Sample_Year:LakeID), data=redOWCSLAP)
summary(redOWTC)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## True_Color ~ Dreissenids + log(CA.SA) + log(Mean_Depth_m) + Percent_Ag +
## (1 | LakeID) + +(1 | Sample_Year:LakeID)
## Data: redOWCSLAP
##
## REML criterion at convergence: 3952.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.2517 -0.4869 -0.0590 0.4123 5.1635
##
## Random effects:
## Groups Name Variance Std.Dev.
## Sample_Year:LakeID (Intercept) 51.704 7.191
## LakeID (Intercept) 2.986 1.728
## Residual 19.507 4.417
## Number of obs: 638, groups: Sample_Year:LakeID, 83; LakeID, 16
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2.05468 4.85067 8.77365 -0.424 0.6821
## DreissenidsUninvaded -0.31734 1.90964 12.20964 -0.166 0.8707
## log(CA.SA) 2.26571 1.04471 9.29823 2.169 0.0573 .
## log(Mean_Depth_m) 3.88331 2.83447 8.76431 1.370 0.2047
## Percent_Ag 0.12086 0.06175 9.82501 1.957 0.0793 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) DrssnU l(CA.S l(M_D_
## DrssndsUnnv 0.028
## log(CA.SA) 0.045 -0.135
## lg(Mn_Dpt_) -0.792 -0.136 -0.568
## Percent_Ag -0.479 0.004 0.087 0.172plot(redOWTC)
qqPlot(resid(redOWTC))
## 318 311
## 140 135Shoreline Bloom Chlorophyll a
-Sample_Year removed for low variance
redSBChl<-lmer(log(ESF_FP_Chl.a) ~ Dreissenids + log(CA.SA) + log(Mean_Depth_m) + Percent_Ag + (1|Sample_Month) + (1|LakeID) , data=redSBCSLAP)
summary(redSBChl)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(ESF_FP_Chl.a) ~ Dreissenids + log(CA.SA) + log(Mean_Depth_m) +
## Percent_Ag + (1 | Sample_Month) + (1 | LakeID)
## Data: redSBCSLAP
##
## REML criterion at convergence: 340.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.48791 -0.52741 0.08567 0.46286 1.99954
##
## Random effects:
## Groups Name Variance Std.Dev.
## LakeID (Intercept) 2.874 1.695
## Sample_Month (Intercept) 1.533 1.238
## Residual 4.449 2.109
## Number of obs: 76, groups: LakeID, 10; Sample_Month, 6
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.65091 4.44514 7.24487 2.171 0.0652 .
## DreissenidsUninvaded -0.48538 1.25697 13.04832 -0.386 0.7056
## log(CA.SA) 0.50360 0.71085 4.15016 0.708 0.5164
## log(Mean_Depth_m) -2.30428 2.28956 5.27136 -1.006 0.3581
## Percent_Ag -0.02415 0.05905 5.89368 -0.409 0.6970
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) DrssnU l(CA.S l(M_D_
## DrssndsUnnv -0.271
## log(CA.SA) 0.114 -0.059
## lg(Mn_Dpt_) -0.823 0.019 -0.509
## Percent_Ag -0.609 0.398 -0.107 0.282plot(redSBChl)
qqPlot(resid(redSBChl))
## 777 319
## 58 26Shoreline Bloom Microcystin
Same as for the global dataset, we need to use average yearly TN:TP values
#Extracting average annual TP for each lake
library(plyr)
redavgTNTP<-ddply(redOWCSLAP, c("LakeID", "Sample_Year"), summarize,
Mean = mean(TN_TP, na.rm=TRUE))
colnames(redavgTNTP)[colnames(redavgTNTP)=="Mean"] <- "TN_TP"
#Merge these values to the SBCSLAP df
redSBCSLAP<-redSBCSLAP[,c(1:49, 51:54)]
redSBCSLAP<-merge(redSBCSLAP, redavgTNTP, by=c("LakeID", "Sample_Year"), all.x=TRUE, all.y=FALSE)Full model error: failed to converge
Remove Sample_Month, LakeID, and Sample_Year:LakeID to resolve
redSBmicro<-lmer(log(ESF_Microcystin) ~ Dreissenids + log(TN_TP) + log(CA.SA) + log(Mean_Depth_m) + Percent_Ag + (1|Sample_Year) , data=redSBCSLAP)## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficientssummary(redSBmicro)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(ESF_Microcystin) ~ Dreissenids + log(TN_TP) + log(CA.SA) +
## log(Mean_Depth_m) + Percent_Ag + (1 | Sample_Year)
## Data: redSBCSLAP
##
## REML criterion at convergence: 55.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.94530 -0.51204 0.04226 0.55293 1.67320
##
## Random effects:
## Groups Name Variance Std.Dev.
## Sample_Year (Intercept) 0.8282 0.9101
## Residual 1.9655 1.4020
## Number of obs: 20, groups: Sample_Year, 5
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 39.5787 87.0165 12.0088 0.455 0.657
## DreissenidsUninvaded 29.8715 73.6454 9.9475 0.406 0.694
## log(TN_TP) -0.4677 1.1793 0.8078 -0.397 0.771
## log(CA.SA) -21.8210 57.3272 10.2002 -0.381 0.711
##
## Correlation of Fixed Effects:
## (Intr) DrssnU l(TN_T
## DrssndsUnnv 0.998
## log(TN_TP) 0.553 0.597
## log(CA.SA) -0.999 -1.000 -0.591
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficientsplot(redSBmicro)
qqPlot(resid(redSBmicro))
## 25 19
## 15 13Open Water Microcystin
redOWmicro<-lmer(log(ESF_Microcystin) ~ Dreissenids + TN_TP + log(CA.SA) + log(Mean_Depth_m) + Percent_Ag + (1|Sample_Year:LakeID), data=redOWCSLAP)
summary(redOWmicro)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## log(ESF_Microcystin) ~ Dreissenids + TN_TP + log(CA.SA) + log(Mean_Depth_m) +
## Percent_Ag + (1 | Sample_Year:LakeID)
## Data: redOWCSLAP
##
## REML criterion at convergence: 47.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.00880 -0.47752 -0.02892 0.24782 2.81800
##
## Random effects:
## Groups Name Variance Std.Dev.
## Sample_Year:LakeID (Intercept) 0.06344 0.2519
## Residual 0.12158 0.3487
## Number of obs: 26, groups: Sample_Year:LakeID, 17
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.368785 0.674275 14.800810 0.547 0.5926
## DreissenidsUninvaded -0.217583 0.215947 14.767464 -1.008 0.3299
## TN_TP -0.009852 0.004625 17.451266 -2.130 0.0477 *
## log(CA.SA) -0.007361 0.138798 16.645875 -0.053 0.9583
## log(Mean_Depth_m) -0.546020 0.345865 13.397873 -1.579 0.1377
## Percent_Ag 0.014426 0.006658 16.414308 2.167 0.0453 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) DrssnU TN_TP l(CA.S l(M_D_
## DrssndsUnnv -0.329
## TN_TP -0.193 0.318
## log(CA.SA) -0.007 -0.400 -0.522
## lg(Mn_Dpt_) -0.882 0.290 0.237 -0.346
## Percent_Ag -0.432 0.152 -0.264 0.094 0.243plot(redOWmicro)
qqPlot(resid(redOWmicro))
## 225 1270
## 7 26HABs Frequency
#Global Dataset
#create column based on 25 ug/L BGA guideline (from NYS DEC)
CSLAP$Bloom <- ifelse(CSLAP$ESF_FP_BGA >=25.0, "Bloom", "No Bloom")
CSLAP$Bloom <- as.factor(CSLAP$Bloom)
#Separate based on invasion status and drop unused levels of Lake Name
I.CSLAP<-CSLAP[CSLAP$Dreissenids == "Invaded",]
I.CSLAP$Lake_Name<-droplevels(I.CSLAP$Lake_Name)
U.CSLAP<-CSLAP[CSLAP$Dreissenids == "Uninvaded",]
U.CSLAP$Lake_Name<-droplevels(U.CSLAP$Lake_Name)
#Reduced Dataset
#create column based on 25 ug/L BGA guideline (from NYS DEC)
redCSLAP$Bloom <- ifelse(redCSLAP$ESF_FP_BGA >=25.0, "Bloom", "No Bloom")
redCSLAP$Bloom <- as.factor(redCSLAP$Bloom)
#Separate based on invasion status and drop unused levels of Lake Name
I.redCSLAP<-redCSLAP[redCSLAP$Dreissenids == "Invaded",]
I.redCSLAP$Lake_Name<-droplevels(I.redCSLAP$Lake_Name)
U.redCSLAP<-redCSLAP[redCSLAP$Dreissenids == "Uninvaded",]
U.redCSLAP$Lake_Name<-droplevels(U.redCSLAP$Lake_Name)Global Dataset: All Blooms
table(CSLAP[, c("Bloom", "Dreissenids")])## Dreissenids
## Bloom Invaded Uninvaded
## Bloom 42 98
## No Bloom 326 2355HABFreqYear<-as.data.frame(table(CSLAP[,c("Bloom","Dreissenids","Sample_Year")]))
HABFreqLake<-as.data.frame(table(CSLAP[,c("Bloom","Dreissenids","Lake_Name")]))
HABFreq<-as.data.frame(table(CSLAP[,c("Bloom","Dreissenids","Sample_Year", "Lake_Name")]))
#separate by invaded and uninvaded
I.blooms<-as.data.frame(table(I.CSLAP[,c("Lake_Name","Sample_Year", "Bloom")]))
I.blooms$Dreissenids<-rep("Invaded", 96)
U.blooms<-as.data.frame(table(U.CSLAP[,c("Lake_Name","Sample_Year", "Bloom")]))
U.blooms$Dreissenids<-rep("Uninvaded", 744)
#Make into one dataframe for analysis
blooms<-rbind(I.blooms, U.blooms)
#Remove 'no blooms'
blooms<-blooms[blooms$Bloom == "Bloom",]AOV
fit<-aov(Freq ~ Dreissenids, data=blooms)
summary(fit)## Df Sum Sq Mean Sq F value Pr(>F)
## Dreissenids 1 15.9 15.901 12.37 0.000485 ***
## Residuals 418 537.4 1.286
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(fit)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Freq ~ Dreissenids, data = blooms)
##
## $Dreissenids
## diff lwr upr p adj
## Uninvaded-Invaded -0.6115591 -0.9533921 -0.2697262 0.0004849GLMER poisson
hist(blooms$Freq)
model<-glmer(Freq ~ Dreissenids + (1|Lake_Name) + (1|Sample_Year:Lake_Name), family=poisson(link=log), data=blooms)
summary(model)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: Freq ~ Dreissenids + (1 | Lake_Name) + (1 | Sample_Year:Lake_Name)
## Data: blooms
##
## AIC BIC logLik deviance df.resid
## 482.6 498.8 -237.3 474.6 416
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.04168 -0.09715 -0.07750 -0.07750 1.79741
##
## Random effects:
## Groups Name Variance Std.Dev.
## Sample_Year:Lake_Name (Intercept) 12.22 3.496
## Lake_Name (Intercept) 1.51 1.229
## Number of obs: 420, groups: Sample_Year:Lake_Name, 408; Lake_Name, 68
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.4006 1.3009 -3.383 0.000718 ***
## DreissenidsUninvaded -0.5865 0.7018 -0.836 0.403307
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## DrssndsUnnv -0.652res<-simulateResiduals(model)
plot(res)
testResiduals(res)
## $uniformity
##
## One-sample Kolmogorov-Smirnov test
##
## data: simulationOutput$scaledResiduals
## D = 0.038304, p-value = 0.5687
## alternative hypothesis: two-sided
##
##
## $dispersion
##
## DHARMa nonparametric dispersion test via sd of residuals fitted vs.
## simulated
##
## data: simulationOutput
## ratioObsSim = 0.0084391, p-value < 2.2e-16
## alternative hypothesis: two.sided
##
##
## $outliers
##
## DHARMa outlier test based on exact binomial test
##
## data: simulationOutput
## outLow = 0.0000e+00, outHigh = 0.0000e+00, nobs = 4.2000e+02, freqH0 =
## 3.9841e-03, p-value = 0.374
## alternative hypothesis: two.sided## $uniformity
##
## One-sample Kolmogorov-Smirnov test
##
## data: simulationOutput$scaledResiduals
## D = 0.038304, p-value = 0.5687
## alternative hypothesis: two-sided
##
##
## $dispersion
##
## DHARMa nonparametric dispersion test via sd of residuals fitted vs.
## simulated
##
## data: simulationOutput
## ratioObsSim = 0.0084391, p-value < 2.2e-16
## alternative hypothesis: two.sided
##
##
## $outliers
##
## DHARMa outlier test based on exact binomial test
##
## data: simulationOutput
## outLow = 0.0000e+00, outHigh = 0.0000e+00, nobs = 4.2000e+02, freqH0 =
## 3.9841e-03, p-value = 0.374
## alternative hypothesis: two.sidedtestZeroInflation(res)
##
## DHARMa zero-inflation test via comparison to expected zeros with
## simulation under H0 = fitted model
##
## data: simulationOutput
## ratioObsSim = 0.99977, p-value = 0.96
## alternative hypothesis: two.sidedReduced Dataset: All Blooms
redHABFreqYear<-as.data.frame(table(redCSLAP[,c("Bloom","Dreissenids","Sample_Year")]))
##Data organization for analysis
#separate by invaded and uninvaded
I.redblooms<-as.data.frame(table(I.redCSLAP[,c("Lake_Name","Sample_Year","Bloom")]))
I.redblooms$Dreissenids<-rep("Invaded", 96)
U.redblooms<-as.data.frame(table(U.redCSLAP[,c("Lake_Name","Sample_Year","Bloom")]))
U.redblooms$Dreissenids<-rep("Uninvaded", 120)
#Make into one dataframe for analysis
redblooms<-rbind(I.redblooms, U.redblooms)
#Remove 'no blooms'
redblooms<-redblooms[redblooms$Bloom == "Bloom",]AOV
redfit<-aov(Freq ~ Dreissenids, data=redblooms)
summary(redfit)## Df Sum Sq Mean Sq F value Pr(>F)
## Dreissenids 1 13.38 13.380 6.767 0.0106 *
## Residuals 106 209.58 1.977
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(redfit)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Freq ~ Dreissenids, data = redblooms)
##
## $Dreissenids
## diff lwr upr p adj
## Uninvaded-Invaded -0.7083333 -1.248186 -0.1684803 0.0106128GLMER poisson
hist(redblooms$Freq)
redmodel<-glmer(Freq ~ Dreissenids + (1|Lake_Name) + (1|Sample_Year:Lake_Name), family=poisson(link=log), data=redblooms)
summary(redmodel)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: Freq ~ Dreissenids + (1 | Lake_Name) + (1 | Sample_Year:Lake_Name)
## Data: redblooms
##
## AIC BIC logLik deviance df.resid
## 163.2 173.9 -77.6 155.2 104
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.9165 -0.3324 -0.2376 -0.2098 2.4299
##
## Random effects:
## Groups Name Variance Std.Dev.
## Sample_Year:Lake_Name (Intercept) 1.016 1.008
## Lake_Name (Intercept) 2.024 1.423
## Number of obs: 108, groups: Sample_Year:Lake_Name, 96; Lake_Name, 16
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.1308 0.7507 -2.838 0.00454 **
## DreissenidsUninvaded -0.3365 0.7764 -0.433 0.66470
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## DrssndsUnnv -0.658redres<-simulateResiduals(redmodel)
plot(redres)
testResiduals(redres)
## $uniformity
##
## One-sample Kolmogorov-Smirnov test
##
## data: simulationOutput$scaledResiduals
## D = 0.083157, p-value = 0.444
## alternative hypothesis: two-sided
##
##
## $dispersion
##
## DHARMa nonparametric dispersion test via sd of residuals fitted vs.
## simulated
##
## data: simulationOutput
## ratioObsSim = 1.1098, p-value = 0.488
## alternative hypothesis: two.sided
##
##
## $outliers
##
## DHARMa outlier test based on exact binomial test
##
## data: simulationOutput
## outLow = 0.0000e+00, outHigh = 0.0000e+00, nobs = 1.0800e+02, freqH0 =
## 3.9841e-03, p-value = 1
## alternative hypothesis: two.sided## $uniformity
##
## One-sample Kolmogorov-Smirnov test
##
## data: simulationOutput$scaledResiduals
## D = 0.083157, p-value = 0.444
## alternative hypothesis: two-sided
##
##
## $dispersion
##
## DHARMa nonparametric dispersion test via sd of residuals fitted vs.
## simulated
##
## data: simulationOutput
## ratioObsSim = 1.1098, p-value = 0.488
## alternative hypothesis: two.sided
##
##
## $outliers
##
## DHARMa outlier test based on exact binomial test
##
## data: simulationOutput
## outLow = 0.0000e+00, outHigh = 0.0000e+00, nobs = 1.0800e+02, freqH0 =
## 3.9841e-03, p-value = 1
## alternative hypothesis: two.sidedtestZeroInflation(redres)
##
## DHARMa zero-inflation test via comparison to expected zeros with
## simulation under H0 = fitted model
##
## data: simulationOutput
## ratioObsSim = 1.0262, p-value = 0.856
## alternative hypothesis: two.sidedGlobal Dataset: HT Blooms
HTBloom<-CSLAP[which(CSLAP$Bloom == "Bloom"), ]
#create df of only OW blooms, then use 10 ug/L microcystin to get HT blooms (DEC open water guideline)
OWHTBloom<-HTBloom[HTBloom$Info_Type == "OW",]
OWHTBloom$HTBloom <- ifelse(OWHTBloom$ESF_Microcystin >= 10.0, "HT Bloom", "No HT Bloom")
#create df of only SB blooms, then use 20 ug/L microcystin to get HT blooms (DEC shoreline guideline)
SBHTBloom<-HTBloom[HTBloom$Info_Type == "SB",]
SBHTBloom$HTBloom <- ifelse(SBHTBloom$ESF_Microcystin >= 20.0, "HT Bloom", "No HT Bloom")
#combine OW and SB dfs
allHTBloom<-rbind(OWHTBloom, SBHTBloom)
#Separate based on invasion status and drop unused levels of Lake Name
I.HTBloom<-allHTBloom[allHTBloom$Dreissenids == "Invaded",]
I.HTBloom$Lake_Name<-droplevels(I.HTBloom$Lake_Name)
U.HTBloom<-allHTBloom[allHTBloom$Dreissenids == "Uninvaded",]
U.HTBloom$Lake_Name<-droplevels(U.HTBloom$Lake_Name)#separate by invaded and uninvaded
I.HTblooms<-as.data.frame(table(I.HTBloom[,c("Lake_Name","Sample_Year","HTBloom")]))
I.HTblooms$Dreissenids<-rep("Invaded", 40)
U.HTblooms<-as.data.frame(table(U.HTBloom[,c("Lake_Name","Sample_Year","HTBloom")]))
U.HTblooms$Dreissenids<-rep("Uninvaded", 348)
#Make into one dataframe for analysis
HTblooms<-rbind(I.HTblooms, U.HTblooms)
#Remove 'no blooms'
HTblooms<-HTblooms[HTblooms$HTBloom == "HT Bloom",]AOV
HTfit<-aov(Freq ~ Dreissenids, data=HTblooms)
summary(HTfit)## Df Sum Sq Mean Sq F value Pr(>F)
## Dreissenids 1 6.14 6.14 11.8 0.000726 ***
## Residuals 192 99.90 0.52
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(HTfit)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Freq ~ Dreissenids, data = HTblooms)
##
## $Dreissenids
## diff lwr upr p adj
## Uninvaded-Invaded -0.5850575 -0.9209807 -0.2491342 0.0007256GLMER poisson
hist(HTblooms$Freq)
HTmodel<-glmer(Freq ~ Dreissenids + (1|Lake_Name) + (1|Sample_Year:Lake_Name), family=poisson(link=log), data=HTblooms)
summary(HTmodel)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: Freq ~ Dreissenids + (1 | Lake_Name) + (1 | Sample_Year:Lake_Name)
## Data: HTblooms
##
## AIC BIC logLik deviance df.resid
## 108.8 121.8 -50.4 100.8 190
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.12433 -0.01921 -0.01921 -0.01921 1.94337
##
## Random effects:
## Groups Name Variance Std.Dev.
## Sample_Year:Lake_Name (Intercept) 0.4006 0.6329
## Lake_Name (Intercept) 27.6627 5.2595
## Number of obs: 194, groups: Sample_Year:Lake_Name, 189; Lake_Name, 32
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.737 3.536 -1.340 0.180
## DreissenidsUninvaded -3.107 2.977 -1.044 0.297
##
## Correlation of Fixed Effects:
## (Intr)
## DrssndsUnnv -0.793HTres<-simulateResiduals(HTmodel)
plot(HTres)
testResiduals(HTres)
## $uniformity
##
## One-sample Kolmogorov-Smirnov test
##
## data: simulationOutput$scaledResiduals
## D = 0.07558, p-value = 0.2177
## alternative hypothesis: two-sided
##
##
## $dispersion
##
## DHARMa nonparametric dispersion test via sd of residuals fitted vs.
## simulated
##
## data: simulationOutput
## ratioObsSim = 0.00091238, p-value = 0.184
## alternative hypothesis: two.sided
##
##
## $outliers
##
## DHARMa outlier test based on exact binomial test
##
## data: simulationOutput
## outLow = 0.0000e+00, outHigh = 0.0000e+00, nobs = 1.9400e+02, freqH0 =
## 3.9841e-03, p-value = 0.9219
## alternative hypothesis: two.sided## $uniformity
##
## One-sample Kolmogorov-Smirnov test
##
## data: simulationOutput$scaledResiduals
## D = 0.07558, p-value = 0.2177
## alternative hypothesis: two-sided
##
##
## $dispersion
##
## DHARMa nonparametric dispersion test via sd of residuals fitted vs.
## simulated
##
## data: simulationOutput
## ratioObsSim = 0.00091238, p-value = 0.184
## alternative hypothesis: two.sided
##
##
## $outliers
##
## DHARMa outlier test based on exact binomial test
##
## data: simulationOutput
## outLow = 0.0000e+00, outHigh = 0.0000e+00, nobs = 1.9400e+02, freqH0 =
## 3.9841e-03, p-value = 0.9219
## alternative hypothesis: two.sidedtestZeroInflation(HTres)
##
## DHARMa zero-inflation test via comparison to expected zeros with
## simulation under H0 = fitted model
##
## data: simulationOutput
## ratioObsSim = 1.0384, p-value = 0.488
## alternative hypothesis: two.sidedReduced Dataset: HT Blooms
redHTBloom<-redCSLAP[which(redCSLAP$Bloom == "Bloom"), ]
#create df of only OW blooms, then use 10 ug/L microcystin to get HT blooms (DEC open water guideline)
redOWHTBloom<-redHTBloom[redHTBloom$Info_Type == "OW",]
redOWHTBloom$HTBloom <- ifelse(redOWHTBloom$ESF_Microcystin >= 10.0, "HT Bloom", "No HT Bloom")
#create df of only SB blooms, then use 20 ug/L microcystin to get HT blooms (DEC shoreline guideline)
redSBHTBloom<-redHTBloom[redHTBloom$Info_Type == "SB",]
redSBHTBloom$HTBloom <- ifelse(redSBHTBloom$ESF_Microcystin >= 20.0, "HT Bloom", "No HT Bloom")
#combine OW and SB dfs
redallHTBloom<-rbind(redOWHTBloom, redSBHTBloom)
#Separate based on invasion status and drop unused levels of Lake Name
redI.HTBloom<-redallHTBloom[redallHTBloom$Dreissenids == "Invaded",]
redI.HTBloom$Lake_Name<-droplevels(redI.HTBloom$Lake_Name)
redU.HTBloom<-redallHTBloom[redallHTBloom$Dreissenids == "Uninvaded",]
redU.HTBloom$Lake_Name<-droplevels(redU.HTBloom$Lake_Name)#separate by invaded and uninvaded
redI.HTblooms<-as.data.frame(table(redI.HTBloom[,c("Lake_Name","Sample_Year", "HTBloom")]))
redI.HTblooms$Dreissenids<-rep("Invaded", 40)
redU.HTblooms<-as.data.frame(table(redU.HTBloom[,c("Lake_Name","Sample_Year", "HTBloom")]))
redU.HTblooms$Dreissenids<-rep("Uninvaded", 35)
#Make into one dataframe for analysis
redHTblooms<-rbind(redI.HTblooms, redU.HTblooms)
#Remove 'no blooms'
redHTblooms<-redHTblooms[redHTblooms$HTBloom == "HT Bloom",]AOV
redHTfit<-aov(Freq ~ Dreissenids, data=redHTblooms)
summary(redHTfit)## Df Sum Sq Mean Sq F value Pr(>F)
## Dreissenids 1 5.74 5.738 7.763 0.00738 **
## Residuals 53 39.17 0.739
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(redHTfit)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Freq ~ Dreissenids, data = redHTblooms)
##
## $Dreissenids
## diff lwr upr p adj
## Uninvaded-Invaded -0.6714286 -1.154771 -0.188086 0.0073825GLMER poisson
hist(redHTblooms$Freq)
redHTmodel<-glmer(Freq ~ Dreissenids + (1|Lake_Name) + (1|Sample_Year:Lake_Name), family=poisson(link=log), data=redHTblooms)
summary(redHTmodel)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: Freq ~ Dreissenids + (1 | Lake_Name) + (1 | Sample_Year:Lake_Name)
## Data: redHTblooms
##
## AIC BIC logLik deviance df.resid
## 51.2 59.3 -21.6 43.2 51
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.72112 -0.22040 -0.08331 -0.08331 3.01644
##
## Random effects:
## Groups Name Variance Std.Dev.
## Sample_Year:Lake_Name (Intercept) 0.1473 0.3838
## Lake_Name (Intercept) 3.8701 1.9672
## Number of obs: 55, groups: Sample_Year:Lake_Name, 50; Lake_Name, 10
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.077 1.412 -1.471 0.141
## DreissenidsUninvaded -2.758 1.834 -1.504 0.133
##
## Correlation of Fixed Effects:
## (Intr)
## DrssndsUnnv -0.469redHTres<-simulateResiduals(redHTmodel)
plot(redHTres)
testResiduals(redHTres)
## $uniformity
##
## One-sample Kolmogorov-Smirnov test
##
## data: simulationOutput$scaledResiduals
## D = 0.12652, p-value = 0.315
## alternative hypothesis: two-sided
##
##
## $dispersion
##
## DHARMa nonparametric dispersion test via sd of residuals fitted vs.
## simulated
##
## data: simulationOutput
## ratioObsSim = 0.86448, p-value = 0.608
## alternative hypothesis: two.sided
##
##
## $outliers
##
## DHARMa outlier test based on exact binomial test
##
## data: simulationOutput
## outLow = 0.0000000, outHigh = 0.0000000, nobs = 55.0000000, freqH0 =
## 0.0039841, p-value = 1
## alternative hypothesis: two.sided## $uniformity
##
## One-sample Kolmogorov-Smirnov test
##
## data: simulationOutput$scaledResiduals
## D = 0.12652, p-value = 0.315
## alternative hypothesis: two-sided
##
##
## $dispersion
##
## DHARMa nonparametric dispersion test via sd of residuals fitted vs.
## simulated
##
## data: simulationOutput
## ratioObsSim = 0.86448, p-value = 0.608
## alternative hypothesis: two.sided
##
##
## $outliers
##
## DHARMa outlier test based on exact binomial test
##
## data: simulationOutput
## outLow = 0.0000000, outHigh = 0.0000000, nobs = 55.0000000, freqH0 =
## 0.0039841, p-value = 1
## alternative hypothesis: two.sidedtestZeroInflation(redHTres)
##
## DHARMa zero-inflation test via comparison to expected zeros with
## simulation under H0 = fitted model
##
## data: simulationOutput
## ratioObsSim = 0.99635, p-value = 1
## alternative hypothesis: two.sidedHABS Frequency Plots
#change column name of HTblooms so that we can combine HTblooms with blooms
colnames(HTblooms)[3] <- "Bloom"
#combine dfs
allblooms<-rbind(HTblooms, blooms)
#remove columns where 0 is reported?
allposblooms<-allblooms[allblooms$Freq > 0, ]ggplot(subset(allposblooms, Bloom == "Bloom"), aes(x=Sample_Year, y=Freq, fill=Dreissenids, color=Dreissenids))+
geom_boxplot()+
theme_minimal()+
scale_y_continuous(breaks= c(0,2, 4, 6, 8, 10))+
scale_fill_manual(values=c("#E7E7E7", "#FFFFFF"))+
scale_color_manual(values=c("#000000", "#717171"))+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"))+
labs(y= "Frequency", x = "Year")+
theme(axis.text.y = element_text(size=12), axis.text.x = element_text(size=12))+
theme(axis.title.x = element_text(size=14), axis.title.y = element_text(size=14))+
theme(legend.title = element_text(size=14), legend.text = element_text(size=12))
ggsave("./output_figures/Bloom Freq Boxplot.png")## Saving 7 x 5 in imagelibrary(RColorBrewer)
ggplot(subset(allposblooms, Bloom == "HT Bloom"), aes(x=Sample_Year, y=Freq, color=Dreissenids, fill=Dreissenids))+
geom_boxplot()+
theme_minimal()+
scale_y_continuous(breaks= c(0,2, 4, 6, 8, 10))+
scale_fill_manual(values=c("#E7E7E7", "#FFFFFF"))+
scale_color_manual(values=c("#000000", "#717171"))+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"))+
labs(y= "Frequency", x = "Year")+
theme(axis.text.y = element_text(size=12), axis.text.x = element_text(size=12))+
theme(axis.title.x = element_text(size=14), axis.title.y = element_text(size=14))+
theme(legend.title = element_text(size=14), legend.text = element_text(size=12))
ggsave("./output_figures/HT Bloom Freq Boxplot.png")## Saving 7 x 5 in imageLinear Regressions
Global Dataset
#Extracting average annual TP for each lake
library(plyr)
avgTP<-ddply(OWCSLAP, c("LakeID", "Sample_Year"), summarize,
Mean = mean(TP, na.rm=TRUE))
#Extracting average annual TN:TP for each lake
avgTNTP<-ddply(OWCSLAP, c("LakeID", "Sample_Year"), summarize,
Mean = mean(TN_TP, na.rm=TRUE))
#Taking only rows where there is a value for Microcystin
Bloom<-CSLAP[!is.na(CSLAP$ESF_Microcystin),]
#Which rows are missing TP values?
noTPbloom<-Bloom[is.na(Bloom$TP),]
#Which rows are missing TN:TP values?
noTNTPbloom<-Bloom[is.na(Bloom$TN_TP),]
#merge average TP to noTPbloom data frame
avgTPbloom<-merge(noTPbloom, avgTP, by=c("LakeID", "Sample_Year"), all.x=TRUE)
#merge average TN:TP to noTNTPbloom data frame
avgTNTPbloom<-merge(noTNTPbloom, avgTNTP, by=c("LakeID", "Sample_Year"), all.x = TRUE)
#remove original TP column, and re-name the average TP column
avgTPbloom<-avgTPbloom[, c(1:16,18:56)]
colnames(avgTPbloom)[colnames(avgTPbloom)=="Mean"] <- "TP"
#remove original TN:TP column, and re-name the average TN:TP column
avgTNTPbloom<-avgTNTPbloom[, c(1:49, 51:56)]
colnames(avgTNTPbloom)[colnames(avgTNTPbloom)=="Mean"] <- "TN_TP"
#re-order columns
avgTPbloom<-avgTPbloom[, c(1:16,55,17:54)]
#re-order columns
avgTNTPbloom<-avgTNTPbloom[, c(1:49,55,50:54)]
#remove TN_TP from avgTPbloom and merge with avgTNTP bloom
avgTPbloom<-avgTPbloom[,c(1:49,51:55)]
TNTPmerge<-avgTNTPbloom[,c(5,50)]
avgTPTNTPbloom<-merge(avgTPbloom, TNTPmerge, by="Sample_Name")
#re-order columns
avgTPTNTPbloom<-avgTPTNTPbloom[,c(1:49,55,50:54)]
#combine two data frames
hadTP<-Bloom[!is.na(Bloom$TP),]
completeBloom<-rbind(hadTP, avgTPTNTPbloom)
#Split by Info_Type
OWcompleteBloom<-completeBloom[completeBloom$Info_Type == "OW",]
SBcompleteBloom<-completeBloom[completeBloom$Info_Type == "SB",]
#split by invasion status
OWinvadedBlooms<-OWcompleteBloom[OWcompleteBloom$Dreissenids == "Invaded",]
OWuninvadedBlooms<-OWcompleteBloom[OWcompleteBloom$Dreissenids == "Uninvaded",]
SBinvadedBlooms<-SBcompleteBloom[SBcompleteBloom$Dreissenids == "Invaded",]
SBuninvadedBlooms<-SBcompleteBloom[SBcompleteBloom$Dreissenids == "Uninvaded",]TP and Microcystin
Open Water
#create linear models
OWinvaded.lm<-lm(log(OWinvadedBlooms$ESF_Microcystin+.1)~log(OWinvadedBlooms$TP+.1))
OWuninvaded.lm<-lm(log(OWuninvadedBlooms$ESF_Microcystin+.1)~log(OWuninvadedBlooms$TP+.1))
summary(OWinvaded.lm)##
## Call:
## lm(formula = log(OWinvadedBlooms$ESF_Microcystin + 0.1) ~ log(OWinvadedBlooms$TP +
## 0.1))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.48252 -0.17973 0.04444 0.11749 0.78628
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.6021 2.0963 4.103 0.00175 **
## log(OWinvadedBlooms$TP + 0.1) 4.2365 0.9805 4.321 0.00121 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3225 on 11 degrees of freedom
## Multiple R-squared: 0.6293, Adjusted R-squared: 0.5956
## F-statistic: 18.67 on 1 and 11 DF, p-value: 0.001213summary(OWuninvaded.lm)##
## Call:
## lm(formula = log(OWuninvadedBlooms$ESF_Microcystin + 0.1) ~ log(OWuninvadedBlooms$TP +
## 0.1))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8498 -0.3228 -0.1828 0.0744 4.3311
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.072 3.407 -1.195 0.235
## log(OWuninvadedBlooms$TP + 0.1) -1.642 1.557 -1.054 0.295
##
## Residual standard error: 0.6902 on 87 degrees of freedom
## Multiple R-squared: 0.01261, Adjusted R-squared: 0.001258
## F-statistic: 1.111 on 1 and 87 DF, p-value: 0.2948OWinvaded.lm.sum<-summary(OWinvaded.lm)
OWuninvaded.lm.sum<-summary(OWuninvaded.lm)
#extract R2 and p-value
r2<-OWinvaded.lm.sum$adj.r.squared
my.p<-OWinvaded.lm.sum$coefficients[2,4]
rp<-vector("expression", 2)
rp[1] <- substitute(expression(italic(R)^2 == MYVALUE),
list(MYVALUE = format(r2,dig=3)))[2]
rp[2] = substitute(expression(italic(p) == MYOTHERVALUE),
list(MYOTHERVALUE = format(my.p, digits = 2)))[2]ggplot(OWcompleteBloom, aes(x=TP, y=ESF_Microcystin, color=Dreissenids, shape=Dreissenids)) +
geom_point(cex=2) +
geom_smooth(data=subset(OWcompleteBloom, Dreissenids == "Invaded"),
aes(x=TP, y=ESF_Microcystin, color=Dreissenids), method="lm", se=FALSE)+
theme_classic()+
scale_x_continuous(trans='log10') +
scale_y_continuous(trans='log10', breaks=c(1, 10, 100, 10000)) +
scale_color_grey()+
theme(axis.text.y = element_text(size=12), axis.text.x = element_text(size=12), axis.title.y = element_text(size=14),
axis.title.x = element_text(size=14), legend.position = "top", legend.title = element_blank(), legend.text = element_text(size=12))+
ylab("log open water microcystin (\u03bcg/L)")+
xlab("log TP (mg/L)")+
annotate(geom="text", x=.0075, y=10, label= "log microcystin = 8.6 + 4.24( log TP)")+
annotate(geom="text", x=.005, y=7, label=rp[1], parse=TRUE)+
annotate(geom="text", x=.005, y=5, label=rp[2], parse=TRUE)## `geom_smooth()` using formula 'y ~ x'## Warning in is.na(x): is.na() applied to non-(list or vector) of type
## 'expression'
## Warning in is.na(x): is.na() applied to non-(list or vector) of type
## 'expression'
ggsave("./output_figures/Fig 2-1 Global OW.png")## Saving 7 x 5 in image
## `geom_smooth()` using formula 'y ~ x'## Warning in is.na(x): is.na() applied to non-(list or vector) of type
## 'expression'
## Warning in is.na(x): is.na() applied to non-(list or vector) of type
## 'expression'Shoreline Bloom
#create linear models
SBinvaded.lm<-lm(log(SBinvadedBlooms$ESF_Microcystin+.1)~log(SBinvadedBlooms$TP+.1))
SBuninvaded.lm<-lm(log(SBuninvadedBlooms$ESF_Microcystin+.1)~log(SBuninvadedBlooms$TP+.1))
summary(SBinvaded.lm)##
## Call:
## lm(formula = log(SBinvadedBlooms$ESF_Microcystin + 0.1) ~ log(SBinvadedBlooms$TP +
## 0.1))
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2455 -0.7569 0.0850 0.8098 2.3340
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.834 16.000 1.240 0.232
## log(SBinvadedBlooms$TP + 0.1) 7.589 7.386 1.027 0.319
##
## Residual standard error: 1.431 on 17 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.05847, Adjusted R-squared: 0.003082
## F-statistic: 1.056 on 1 and 17 DF, p-value: 0.3186summary(SBuninvaded.lm)##
## Call:
## lm(formula = log(SBuninvadedBlooms$ESF_Microcystin + 0.1) ~ log(SBuninvadedBlooms$TP +
## 0.1))
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.1845 -1.8375 -0.3783 1.8765 5.8238
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 34.79 35.71 0.974 0.340
## log(SBuninvadedBlooms$TP + 0.1) 13.95 16.52 0.845 0.407
##
## Residual standard error: 2.653 on 22 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.0314, Adjusted R-squared: -0.01262
## F-statistic: 0.7133 on 1 and 22 DF, p-value: 0.4074SBinvaded.lm.sum<-summary(SBinvaded.lm)
SBuninvaded.lm.sum<-summary(SBuninvaded.lm)ggplot(SBcompleteBloom, aes(x=TP, y=ESF_Microcystin, color=Dreissenids, shape=Dreissenids)) +
geom_point(cex=2) +
theme_classic()+
scale_x_continuous(trans='log10') +
scale_y_continuous(trans='log10', breaks=c(1,100, 10000)) +
scale_color_grey()+
theme(axis.text.y = element_text(size=12), axis.text.x = element_text(size=12), axis.title.y = element_text(size=14),
axis.title.x = element_text(size=14), legend.position = "top", legend.title = element_blank(), legend.text = element_text(size=12))+
ylab("log shoreline bloom microcystin (\u03bcg/L)")+
xlab("log TP (mg/L)")## Warning: Removed 3 rows containing missing values (geom_point).
ggsave("./output_figures/Fig 2-1 Global SB.png")## Saving 7 x 5 in image## Warning: Removed 3 rows containing missing values (geom_point).TN:TP and Microcystin
Open Water
#create linear models
OWTNTP_invaded.lm<-lm(log(OWinvadedBlooms$ESF_Microcystin+.1)~log(OWinvadedBlooms$TN_TP+.1))
OWTNTP_uninvaded.lm<-lm(log(OWuninvadedBlooms$ESF_Microcystin+.1)~log(OWuninvadedBlooms$TN_TP+.1))
summary(OWTNTP_invaded.lm)##
## Call:
## lm(formula = log(OWinvadedBlooms$ESF_Microcystin + 0.1) ~ log(OWinvadedBlooms$TN_TP +
## 0.1))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.37473 -0.23835 -0.22292 -0.06378 1.11484
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.5400 0.7164 0.754 0.467
## log(OWinvadedBlooms$TN_TP + 0.1) -0.2916 0.2077 -1.404 0.188
##
## Residual standard error: 0.4878 on 11 degrees of freedom
## Multiple R-squared: 0.152, Adjusted R-squared: 0.07489
## F-statistic: 1.971 on 1 and 11 DF, p-value: 0.1879summary(OWTNTP_uninvaded.lm)##
## Call:
## lm(formula = log(OWuninvadedBlooms$ESF_Microcystin + 0.1) ~ log(OWuninvadedBlooms$TN_TP +
## 0.1))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8382 -0.2960 -0.2138 0.0185 4.3962
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.8216 0.5559 -1.478 0.143
## log(OWuninvadedBlooms$TN_TP + 0.1) 0.1014 0.1626 0.624 0.534
##
## Residual standard error: 0.6962 on 86 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.004504, Adjusted R-squared: -0.007072
## F-statistic: 0.3891 on 1 and 86 DF, p-value: 0.5344OWTNTP_invaded.lm.sum<-summary(OWTNTP_invaded.lm)
OWTNTP_uninvaded.lm.sum<-summary(OWTNTP_uninvaded.lm)ggplot(OWcompleteBloom, aes(x=TN_TP, y=ESF_Microcystin, color=Dreissenids, shape=Dreissenids)) +
geom_point(cex=2) +
theme_classic()+
scale_x_continuous(trans='log10') +
scale_y_continuous(trans='log10', breaks=c(1, 10,100, 10000)) +
scale_color_grey()+
theme(axis.text.y = element_text(size=12), axis.text.x = element_text(size=12), axis.title.y = element_text(size=14),
axis.title.x = element_text(size=14), legend.position = "top", legend.title = element_blank(), legend.text=element_text(size=12))+
ylab("log open water microcystin (\u03bcg/L)")+
xlab("log TN:TP")## Warning: Removed 1 rows containing missing values (geom_point).
ggsave("./output_figures/Fig 2-3 Global OW.png")## Saving 7 x 5 in image## Warning: Removed 1 rows containing missing values (geom_point).Shoreline Bloom
#create linear models
SBTNTP_invaded.lm<-lm(log(SBinvadedBlooms$ESF_Microcystin+.1)~log(SBinvadedBlooms$TN_TP+.1))
SBTNTP_uninvaded.lm<-lm(log(SBuninvadedBlooms$ESF_Microcystin+.1)~log(SBuninvadedBlooms$TN_TP+.1))
summary(SBTNTP_invaded.lm)##
## Call:
## lm(formula = log(SBinvadedBlooms$ESF_Microcystin + 0.1) ~ log(SBinvadedBlooms$TN_TP +
## 0.1))
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.5667 -0.6502 0.1916 0.7317 2.4407
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.8364 2.3907 2.441 0.0259 *
## log(SBinvadedBlooms$TN_TP + 0.1) -0.5496 0.5338 -1.030 0.3176
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.431 on 17 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.0587, Adjusted R-squared: 0.003334
## F-statistic: 1.06 on 1 and 17 DF, p-value: 0.3176summary(SBTNTP_uninvaded.lm)##
## Call:
## lm(formula = log(SBuninvadedBlooms$ESF_Microcystin + 0.1) ~ log(SBuninvadedBlooms$TN_TP +
## 0.1))
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7351 -1.2655 -0.2051 1.7642 5.8474
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 14.229 8.627 1.649 0.113
## log(SBuninvadedBlooms$TN_TP + 0.1) -2.817 2.529 -1.114 0.277
##
## Residual standard error: 2.622 on 22 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.05339, Adjusted R-squared: 0.01036
## F-statistic: 1.241 on 1 and 22 DF, p-value: 0.2774SBTNTP_invaded.lm.sum<-summary(SBTNTP_invaded.lm)
SBTNTP_uninvaded.lm.sum<-summary(SBTNTP_uninvaded.lm)ggplot(SBcompleteBloom, aes(x=TN_TP, y=ESF_Microcystin, color=Dreissenids, shape=Dreissenids)) +
geom_point(cex=2) +
theme_classic()+
scale_x_continuous(trans='log10') +
scale_y_continuous(trans='log10', breaks=c(1,100, 10000)) +
scale_color_grey()+
theme(axis.text.y = element_text(size=12), axis.text.x = element_text(size=12), axis.title.y = element_text(size=14),
axis.title.x = element_text(size=14), legend.position = "top", legend.title = element_blank(), legend.text=element_text(size=12))+
ylab("log shoreline bloom microcystin (\u03bcg/L)")+
xlab("log TN:TP")## Warning: Removed 3 rows containing missing values (geom_point).
ggsave("./output_figures/Fig 2-3 Global SB.png")## Saving 7 x 5 in image## Warning: Removed 3 rows containing missing values (geom_point).Chlorophyll-a and Microcystin
Open Water
#create linear models
OWchl.invaded.lm<-lm(log(OWinvadedBlooms$ESF_Microcystin+.1)~log(OWinvadedBlooms$Extracted_Chl.a+.1))
OWchl.uninvaded.lm<-lm(log(OWuninvadedBlooms$ESF_Microcystin+.1)~log(OWuninvadedBlooms$Extracted_Chl.a+.1))
summary(OWchl.invaded.lm)##
## Call:
## lm(formula = log(OWinvadedBlooms$ESF_Microcystin + 0.1) ~ log(OWinvadedBlooms$Extracted_Chl.a +
## 0.1))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.38713 -0.32568 -0.15193 0.03177 1.17554
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.3746 0.1675 -2.237 0.0469
## log(OWinvadedBlooms$Extracted_Chl.a + 0.1) -0.1446 0.1739 -0.832 0.4234
##
## (Intercept) *
## log(OWinvadedBlooms$Extracted_Chl.a + 0.1)
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5138 on 11 degrees of freedom
## Multiple R-squared: 0.05914, Adjusted R-squared: -0.02639
## F-statistic: 0.6914 on 1 and 11 DF, p-value: 0.4234summary(OWchl.uninvaded.lm)##
## Call:
## lm(formula = log(OWuninvadedBlooms$ESF_Microcystin + 0.1) ~ log(OWuninvadedBlooms$Extracted_Chl.a +
## 0.1))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8337 -0.3284 -0.1780 0.0239 4.3742
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) -0.38759 0.12036 -3.220
## log(OWuninvadedBlooms$Extracted_Chl.a + 0.1) -0.09242 0.09603 -0.962
## Pr(>|t|)
## (Intercept) 0.00181 **
## log(OWuninvadedBlooms$Extracted_Chl.a + 0.1) 0.33853
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6945 on 86 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.01066, Adjusted R-squared: -0.0008479
## F-statistic: 0.9263 on 1 and 86 DF, p-value: 0.3385OWchl.invaded.lm.sum<-summary(OWchl.invaded.lm)
OWchl.uninvaded.lm.sum<-summary(OWchl.uninvaded.lm)ggplot(OWcompleteBloom, aes(x=Extracted_Chl.a, y=ESF_Microcystin, color=Dreissenids, shape=Dreissenids)) +
geom_point(cex=2) +
theme_classic()+
scale_x_continuous(trans='log10') +
scale_y_continuous(trans='log10') +
scale_color_grey()+
theme(axis.text.y = element_text(size=12), axis.text.x = element_text(size=12), axis.title.y = element_text(size=14),
axis.title.x = element_text(size=14), legend.position = "top", legend.title = element_blank(), legend.text=element_text(size=12))+
ylab("log open water microcystin (\u03bcg/L)")+
xlab("log extracted chlorophyll-a (\u03bcg/L)")## Warning: Removed 1 rows containing missing values (geom_point).
ggsave("./output_figures/Fig 2-2 Global OW.png")## Saving 7 x 5 in image## Warning: Removed 1 rows containing missing values (geom_point).Shoreline Bloom
#create linear models
SBchl.invaded.lm<-lm(log(SBinvadedBlooms$ESF_Microcystin+.1)~log(SBinvadedBlooms$ESF_FP_Chl.a+.1))
SBchl.uninvaded.lm<-lm(log(SBuninvadedBlooms$ESF_Microcystin+.1)~log(SBuninvadedBlooms$ESF_FP_Chl.a+.1))
summary(SBchl.invaded.lm)##
## Call:
## lm(formula = log(SBinvadedBlooms$ESF_Microcystin + 0.1) ~ log(SBinvadedBlooms$ESF_FP_Chl.a +
## 0.1))
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2713 -0.9313 0.2731 1.0336 2.3892
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.7787 1.4482 1.919 0.071 .
## log(SBinvadedBlooms$ESF_FP_Chl.a + 0.1) 0.1318 0.2493 0.529 0.603
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.531 on 18 degrees of freedom
## Multiple R-squared: 0.01529, Adjusted R-squared: -0.03941
## F-statistic: 0.2795 on 1 and 18 DF, p-value: 0.6035summary(SBchl.uninvaded.lm)##
## Call:
## lm(formula = log(SBuninvadedBlooms$ESF_Microcystin + 0.1) ~ log(SBuninvadedBlooms$ESF_FP_Chl.a +
## 0.1))
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.2030 -1.9212 -0.1592 1.7338 5.5123
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.1730 2.4462 0.480 0.636
## log(SBuninvadedBlooms$ESF_FP_Chl.a + 0.1) 0.4346 0.2928 1.484 0.151
##
## Residual standard error: 2.498 on 24 degrees of freedom
## Multiple R-squared: 0.08409, Adjusted R-squared: 0.04593
## F-statistic: 2.204 on 1 and 24 DF, p-value: 0.1507SBchl.invaded.lm.sum<-summary(SBchl.invaded.lm)
SBchl.uninvaded.lm.sum<-summary(SBchl.uninvaded.lm)ggplot(SBcompleteBloom, aes(x=ESF_FP_Chl.a, y=ESF_Microcystin, color=Dreissenids, shape=Dreissenids)) +
geom_point(cex=2) +
theme_classic()+
scale_x_continuous(trans='log10', breaks=c(10,100,1000,10000)) +
scale_y_continuous(trans='log10') +
scale_color_grey()+
theme(axis.text.y = element_text(size=12), axis.text.x = element_text(size=12), axis.title.y = element_text(size=14),
axis.title.x = element_text(size=14), legend.position = "top", legend.title = element_blank(), legend.text=element_text(size=12))+
ylab("log shoreline bloom microcystin (\u03bcg/L)")+
xlab("log fluoroprobe chlorophyll-a (\u03bcg/L)")
ggsave("./output_figures/Fig 2-2 Global SB.png")## Saving 7 x 5 in image